[table]capposition=top

2023 051 21 March

p–p–K femtoscopy

ALICE Collaboration††thanks: See Appendix A for the list of collaboration
members ALICE Collaboration

The interactions of kaons (K) and antikaons ($\mathrm{\overline{K}}$) with few
nucleons (N) were studied so far using kaonic atom data and measurements of
kaon production and interaction yields in nuclei. Some details of the three-
body KNN and $\mathrm{\overline{K}}$NN dynamics are still not well understood,
mainly due to the overlap with multi-nucleon interactions in nuclei. An
alternative method to probe the dynamics of three-body systems with kaons is
to study the final state interaction within triplet of particles emitted in pp
collisions at the Large Hadron Collider, which are free from effects due to
the presence of bound nucleons. This Letter reports the first femtoscopic
study of p–p–K+ and p–p–K- correlations measured in high-multiplicity pp
collisions at $\sqrt{s}$ = 13 TeV by the ALICE Collaboration. The analysis
shows that the measured p–p–K+ and p–p–K- correlation functions can be
interpreted in terms of pairwise interactions in the triplets, indicating that
the dynamics of such systems is dominated by the two-body interactions without
significant contributions from three-body effects or bound states.

The nature of kaons and antikaons is closely related to the chiral symmetry
breaking pattern of low-energy QCD [1, 2]. Therefore, the study of kaon
properties and their modification in dense nuclear matter has received the
attention of the scientific community in the past decades. In particular, the
hadronic interactions of charged kaons, K+ and K-, with nucleons (N) were
investigated using kaonic atoms [3, 4, 5] and by studying the interaction or
the production of kaons in light [6, 7, 8, 9, 10, 11, 12, 13, 14] and heavy
nuclei [15, 16, 17, 18, 19, 20, 21, 22, 23]. Such measurements demonstrated
the repulsive and attractive nature of the K+N and K-N strong interactions [1,
2], respectively. Information on the in-medium modification of the K+ and K-
potentials with the increasing baryon density was extracted from the
comparison of kaon production yields and flow observables measured in
nucleus–nucleus collisions [15, 16, 17] and pion- and proton-induced reactions
[18, 19, 20, 23, 21, 22] with the expectations from transport models. Several
K+ nuclear potentials have been tested and the best agreement with the data
results in a repulsive strength of 20–40 MeV at nuclear saturation density. On
the other hand, the K-N interaction is known to be sufficiently attractive in
the isospin I = 0 channel to dynamically generate the $\Lambda(1405)$ just
below the K-p threshold [24]. Such a state is interpreted as a quasi-bound
antikaon–nucleon $\mathrm{\overline{K}}$N system which couples strongly to the
$\pi\Sigma$ channel, giving rise to a sizable K- absorption [25]. In the case
of K- nuclear interaction, the influence of single- and multi-nucleon
absorption processes in nuclei currently prevents extracting firm conclusions
on the strength of the K- in-medium attractive potential [1]. This ambiguity
triggered a longstanding debate in the literature about the possible existence
of exotic kaonic bound objects with nucleons (see [1] and references therein).
Recently, the E15 Collaboration reported the first experimental evidence of
the $\mathrm{\overline{K}}$NN state with a binding energy of about 42 MeV and
a decay width of about 100 MeV [26]. From the theoretical side, binding
energies in the range 9–95 MeV and decay widths between 16 and 110 MeV are
expected [27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42]. The
uncertainties in the models mainly arise from scarce knowledge of the full
$\mathrm{\overline{K}}$NN three-body effects, such as three-body coupled
channels and two-nucleon absorption processes. Additionally, three-body
forces, which are relevant in the calculation of the nuclear binding energies
[43, 44], are currently not included in kaonic bound state models. Further
experimental investigations on the K+NN and K-NN three-body dynamics, in
addition to the studies of K+ and K- interaction in nuclei, are required to
isolate and quantify the contribution from genuine three-body effects in such
systems.

An alternative method to explore the three-body dynamics of K+ and K- with
nucleons is to employ the femtoscopy technique at high-energy collider
facilities. In small colliding systems, such as pp and p–Pb at the Large
Hadron Collider (LHC), the inter-hadron distances at the time of the particle
emission range from a few femtometers down to scales compatible with the
nucleon size. This leads to an enhancement of the strength of the signal due
to the short-range strong interaction in the measured correlation function
[45]. The femtoscopy method was proven to be able to test and constrain the
hadron–hadron interaction for various two-particle systems [46, 47, 48, 49,
50, 51, 52, 53, 54, 55, 56, 57], providing data with unprecedented precision
on the hadronic interactions with strangeness at low relative momenta, down to
the energy threshold of the produced pairs. Furthermore, the measured
femtoscopic correlation functions are sensitive to the presence of bound
states in the energy region below the threshold. Thus, they were also used to
constrain the parameters of bound hadron–hadron systems [49, 58]. Recently,
the ALICE Collaboration has extended the method to explore the correlation
among three baryons, such as p–p–p and p–p–$\Lambda$, to study the dynamics of
three-body systems [59]. The analysis exploited Kubo’s cumulant expansion
method [60] and the projector method [61] to isolate the genuine three-
particle correlations from the measured correlation functions. As a result, a
first experimental hint of genuine three-body effects in the unbound p–p–p
system was found. The study in this Letter applies the same analysis procedure
adopted in [59] to the case of p–p–K+ and p–p–K- particle triplets to explore
possible genuine three-body effects in the correlation functions induced by
the strong interaction and bound state formation. The main advantage of the
femtoscopy method with respect to the previous experimental techniques is the
possibility to investigate for the first time the K+ and K- three-body
dynamics with nucleons, free from additional effects induced by the presence
of the surrounding nucleons in nuclei.

The analysed data sample of pp collisions at $\sqrt{s}~{}=~{}13$ TeV was
recorded by ALICE [62, 63, 64] during the LHC Run 2 (2015–2018) data-taking
period. In the following, the information from the V0 detector system [65],
the Inner Tracking System (ITS) [66], the Time Projection Chamber (TPC) [67]
and the Time-Of-Flight (TOF) detector [68] is used. The V0 detector is
employed to trigger on high-multiplicity (HM) events. This trigger selects
events within the 0.17% largest charged-track multiplicity of the INEL$>0$
class, which is defined as inelastic collisions with at least one measured
charged particle in the pseudorapidity range $|\eta|<1$ [65]. This condition
results in an average of about 30 charged particles in the range $|\eta|<0.5$
[51], hence increasing the probability of finding triplets of the desired
particle species with respect to the minimum-bias sample. The primary vertex
position is reconstructed with the combined information of the ITS and the
TPC, and, independently, with track segments in the two innermost layers of
the ITS. Only events with a reconstructed primary vertex position along the
beam axis within 10 cm from the centre of the ALICE detector are selected. A
total of 109 HM events are used in this analysis. The p–p–K+ and p–p–K- data
samples are built by combining three charged-particle tracks (triplets)
reconstructed with the TPC. Assuming the same interactions in the particle and
antiparticle systems, triplets of particles and the corresponding
antiparticles are combined: p–p–K+ $\equiv$ ( p–p–K+ $\oplus$
$\rm\overline{p}$–$\rm\overline{p}$–K-) and p–p–K- $\equiv$ ( p–p–K- $\oplus$
$\rm\overline{p}$–$\rm\overline{p}$–K+). The agreement of the corresponding
correlation functions confirmed the validity of this assumption.

Particles and antiparticles are identified using the same kinematic and
topological selections. Protons and kaons are selected in the range
$|\eta|<0.8$ and in the transverse momentum intervals $p_{\rm T}\in$
(0.5–4.05) $\text{Ge\kern-1.00006ptV/}c$ and $p_{\rm T}\in$ (0.2–2.5)
$\text{Ge\kern-1.00006ptV/}c$, respectively. A minimum of 80 space points
(hits) inside the TPC, out of the total 159, is required to guarantee track
quality and good momentum resolution. Particle identification (PID) is
conducted by requiring that the measured energy loss
($\textrm{d}E/\textrm{d}x$) in the TPC gas is compatible with the expected one
from protons and kaons within three standard deviations ($\sigma$). For high
momentum particles, the $\textrm{d}E/\textrm{d}x$ information is combined with
the time-of-flight measurement provided by the TOF, using a 3$\sigma$
selection on the expected value for a given particle hypothesis at a given
momentum. The PID selection for protons is described in detail in [46]. The
selection of kaons is based on the procedure described in [54] with several
changes as explained in the following. TPC reconstructed tracks are identified
as kaons either by using TPC PID information for momenta lower than 0.85
GeV/$c$, or, if a signal in the TOF is matched to the TPC track, by using the
combined PID information from TPC and TOF up to a momentum of 2.5 GeV/$c$. To
improve the purity of the kaon selection, the TPC and TOF information are also
used to reject candidates that are compatible with the pion or electron
hypothesis. To reject particles that are non-primary or come from pile-up
collisions, the distance of closest approach (DCA) to the primary vertex of
the tracks is required to be less than 0.1 cm in the transverse plane and less
than 0.2 cm along the beam axis. The purity of the proton and kaon candidates
is estimated employing Monte Carlo simulations based on PYTHIA 8 [69] (Monash
2013 Tune) event generator with a dedicated high-multiplicity selection to
mimic the V0 high-multiplicity trigger and the Geant4 package [70, 71]. The
purity averaged over the $p_{\rm T}$ ranges of the identified protons and
kaons is about $98\%$ for both particle species.

The number of selected and analysed triplets amounts to 4530 for p–p–K+, 3161
for $\rm\overline{p}$–$\rm\overline{p}$–K-, 6200 for p–p–K- and 4937 for
$\rm\overline{p}$–$\rm\overline{p}$–K+ in the femtoscopic region $Q_{3}<0.4$
$\text{Ge\kern-1.00006ptV/}c$. The kinematic variable $Q_{3}$, which is used
in three-body analyses [59, 72], is a Lorentz-invariant scalar defined as
$Q_{3}=\sqrt{-q_{12}^{2}-q_{23}^{2}-q_{31}^{2}}$, where $q_{ij}$ is the norm
of the relative four-momentum of the pair $ij$ in the triplet $q_{ij}^{\mu}=2\
[m_{j}/(m_{i}~{}+~{}m_{j})~{}p_{i}^{\mu}~{}-~{}m_{i}/(m_{i}+m_{j})\
p_{j}^{\mu}]$. The systematic uncertainties of the correlation functions are
evaluated by performing simultaneous variations of the selection criteria for
particles and antiparticles. For protons and antiprotons, the same variations
for the track selection and PID criteria as in [59] are performed. Similar
variations are used for the kaons and antikaons. In order to account for the
correlations between the systematic uncertainties, the variations are randomly
combined in sets in which at least one selection criterion is changed. Large
statistical fluctuations introduced by the variations of the selection
criteria are avoided by requiring that the yield of the triplets changes by
less than 10% with respect to the standard selection in the femtoscopic region
$Q_{3}<0.4$ $\text{Ge\kern-1.00006ptV/}c$. The main contribution at low
$Q_{3}$ ($\approx 0.15$ $\text{Ge\kern-1.00006ptV/}c$) is given by the
variation of the DCA selection and it is found to be smaller than 5%.

The final state interactions (FSIs) among the hadrons emitted in the
collisions can be explored using correlation functions [73, 74] in momentum
space, defined as

$C_{3}(\mathbf{p}_{1},\mathbf{p}_{2},\mathbf{p}_{3})=\frac{P_{3}(\mathbf{p}_{1},\mathbf{p}_{2},\mathbf{p}_{3})}{P_{1}(\mathbf{p}_{1})P_{1}(\mathbf{p}_{2})P_{1}(\mathbf{p}_{3})}\
,$ (1)

where $\mathbf{p}_{i}$ is the momentum vector of the $i$-th particle and
$P_{3}(\mathbf{p}_{1},\mathbf{p}_{2},\mathbf{p}_{3})$ and
$P_{1}(\mathbf{p}_{i})$ are the probabilities of finding three particles and
one particle with the corresponding momentum, respectively. If the particles
are not correlated
$P_{3}(\mathbf{p}_{1},\mathbf{p}_{2},\mathbf{p}_{3})=P_{1}(\mathbf{p}_{1})P_{1}(\mathbf{p}_{2})P_{1}(\mathbf{p}_{3})$
and then the correlation function becomes identical to unity. The presence of
FSIs induces a correlation signal imprinted in the correlation function which
causes deviations from unity depending on the nature of the interactions,
attractive or repulsive, as well as on the properties of the emitting source
(see [45, 75] for the details). In the case of three-particle systems, the
correlation function can be evaluated in terms of $Q_{3}$. Hence, three-
particle correlation functions are experimentally obtained as

$C_{3}(Q_{3})=\mathcal{N}\
\frac{N_{\mathrm{same}}(Q_{3})}{N_{\mathrm{mixed}}(Q_{3})}\ ,$ (2)

where $\mathcal{N}$ is a normalisation parameter, $N_{\mathrm{same}}(Q_{3})$
is the $Q_{3}$ distribution of the particle triplets emitted in the same
collision and $N_{\mathrm{mixed}}(Q_{3})$ is the distribution of uncorrelated
triplets. The latter are obtained by taking the three particles needed to form
a triplet from three different collisions, thus called mixed event sample.
Only events with similar multiplicity and position of the primary vertex along
the beam axis are mixed. The number of events used for the mixing is set to
30. To account for the two-track merging and splitting effects due to the
finite two-track resolution in the same event sample, a minimum value of the
distance between the same charge tracks on the azimuthal–polar angle plane
$\Delta\eta$–$\Delta\varphi$ is applied to both the same and mixed event
samples. The default selection is
$\Delta\eta^{2}+\Delta\varphi^{2}\geq(0.017)^{2}$ for p–p tracks and
$\Delta\eta^{2}/(0.012)^{2}+\Delta\varphi^{2}/(0.04)^{2}\geq 1$ for p–K+
tracks. A systematic variation of about $+$10% for the values of the minimum
distance is applied in the analysis. The correlation function is normalised to
unity in the range $1.0<Q_{3}<1.2$ GeV/$c$, where no signal from FSIs is
expected. Variations of the normalisation range are performed and included in
the systematic uncertainties. In the case of a particle triplet X–Y–Z,
pairwise correlations with one uncorrelated particle (X–Y)–Z, X–(Y–Z) and
(Z–X)–Y also occur in the system. Genuine three-particle correlations were
isolated in [59] by applying the cumulant expansion method to the femtoscopic
correlation functions. The three-particle cumulant $c_{3}(Q_{3})$, which
incorporates information about the three-body effects in the system, is
computed from the measured correlation function $C_{3}(Q_{3})$ as follows

$c_{3}(Q_{3})=C_{3}(Q_{3})-C_{\mathrm{two\text{-}body}}(Q_{3})\ ,$ (3)

where pairwise correlations are evaluated following the cumulant decomposition
as

$C_{\mathrm{two\text{-}body}}(Q_{3})=C_{\mathrm{{}_{(X-Y)-Z}}}(Q_{3})+C_{\mathrm{{}_{X-(Y-Z)}}}(Q_{3})+C_{\mathrm{{}_{(Z-X)-Y}}}(Q_{3})-2\
.$ (4)

Each component $C_{\mathrm{{}_{(i-j)-k}}}(Q_{3})$ of the lower-order
contributions in Eq. (4) is computed using two methods [59, 61]: $i)$ a data-
driven approach based on the event mixing technique, building triplets in
which the correlated (i–j) pairs are emitted in the same collision and the
uncorrelated particle –k from another collision; $ii)$ the projector method
which uses as input the measured two-particle correlation function $C(k^{*})$
for the correlated pair (i–j), evaluated in terms of the relative momentum
$k^{*}$ in the pair rest frame (PRF), and calculates all the possible $k^{*}$
configurations in the phase space for each $Q_{3}$ value of the i–j–k triplet.
The input correlation functions $C(k^{*})$ used in the projector method are
obtained by selecting p–p, p–K+ and p–K- pairs using the same dataset as for
the three-body analysis. Unlike the p–p and p–K+ pairs, the p–K- correlation
is significantly affected by the contribution from jet-like events [76]. To
use particles emitted in collisions with the same event shape as in the case
of the analysed triplets, p–K- pairs are selected from p–p–K- triplets with
$Q_{3}<1$ GeV/$c$, while for p–p and p–K+ pairs such additional requirement is
not used as the correlation functions with and without the $Q_{3}$ selection
are in agreement. A $\pm 0.1$ GeV/$c$ variation of the $Q_{3}$ limit is
included in the systematic uncertainties of the projector method and it is
combined with the uncertainties propagated from the data points of the p–p,
p–K+ and p–K- correlation functions. The latter include the variations in the
selection criteria for particles, variations in the track splitting/merging
rejection criteria, and normalisation range.

Figure 1: Correlation functions for (p–p)–K+ (panel $\mathrm{a_{1}}$),
(p–p)–K- (panel $\mathrm{a_{2}}$), p–(p–K+) (panel $\mathrm{b_{1}}$), p–(p–K-)
(panel $\mathrm{b_{2}}$) as well as the total lower-order contributions for
p–p–K+ (panel $\mathrm{c_{1}}$) and p–p–K- (panel $\mathrm{c_{2}}$) as a
function of $Q_{3}$. The points represent the results obtained using the data-
driven approach, with the statistical and systematic uncertainties represented
by the error bars and the green boxes, respectively. The grey bands are the
expectations of the projector method, with the band width representing the
combined statistical and systematic uncertainties.

For the p–p–K+ and p–p–K- systems, the lower-order contributions to the three-
particle correlation functions are shown in Fig. 1, using the data-driven
approach (data points) and the projector method (grey bands). The three-
particle correlation functions of two correlated protons and an uncorrelated
kaon, denoted as (p–p)–K+ and (p–p)–K-, are shown in panels $\mathrm{a_{1}}$)
and $\mathrm{a_{2}}$), respectively. In both cases, the correlation functions
are larger than unity in the low $Q_{3}$ region as a result of the attractive
p–p strong interaction, combined with the repulsive Coulomb and quantum
statistics effects (see [46, 48] for more details). In the case of correlated
p–K+ pairs with uncorrelated protons, p–(p–K+), the correlation function shown
in panel $\mathrm{b_{1}}$) is lower than unity as a result of the repulsive
p–K+ strong and Coulomb interactions, consistent with the measurement reported
in [47]. In the p–(p–K-) correlation function, shown in panel
$\mathrm{b_{2}}$), the main features of the p–K- interaction are visible [47]:
the cusp structure due to the opening of the $\mathrm{\overline{K}^{0}}$n
channel as well as the bump due to the $\mathrm{\Lambda(1520)\rightarrow
pK^{-}}$ decay appear at $Q_{3}\approx$ 0.15 GeV/$c$ and $Q_{3}\approx$ 0.7
GeV/$c$, respectively. The total lower-order contributions to the p–p–K+,
panel $\mathrm{c_{1}}$), and p–p–K-, panel $\mathrm{c_{2}}$), correlation
functions evaluated with the data-driven approach and the projector method are
in agreement. The reduced $\chi^{2}$ between the two methods, evaluated in the
$Q_{3}$ range shown in Fig. 1 (21 degrees of freedom) by adding in quadrature
the statistical and systematic uncertainties of the data, is 1.6 and 1.4 for
p–p–K+ and p–p–K-, respectively. Since the projector method does not depend on
the third particle mixing, it provides significantly smaller uncertainties
than the data-driven approach and hence is used to extract the cumulants. This
choice does not affect the final results of the analysis. Figure 2 shows the
measured p–p–K+ (left panel) and p–p–K- (right panel) correlation functions
(data points) compared to the lower-order contributions evaluated using the
projector method (grey bands).

Figure 2: Correlation functions (data points) for p–p–K+ (left panel) and
p–p–K- (right panel) compared to the lower-order contributions evaluated using
the projector method (grey bands). Statistical and systematic uncertainties
are represented by error bars and green boxes, respectively. The band widths
represent the combined statistical and systematic uncertainties propagated
from the two-particle correlation functions used as input to the projector
method.

The corresponding cumulants are extracted using Eq. (3). The measured
cumulants include the correlations of the primary particles as well as the
feed-down from resonances and particle misidentifications, which need to be
accounted for as discussed in [59]. The number of correctly identified primary
triplets is about 66% of the total sample for both p–p–K+ and p–p–K-. The
remaining contribution mainly stems from the feed-down of the $\Lambda$ (17%)
and $\Sigma^{+}$ (7%) hyperon decays into protons and from the $\phi$ meson
(4%) decay into charged kaons. These numbers are obtained combining the
primary and secondary fractions with particle purities. The primary and
secondary fractions are extracted using Monte Carlo template fits to the
measured distributions of the DCA to the primary vertex [46]. The fraction of
charged kaons from $\phi$ decays is calculated using the expectations from a
thermal model [77]. The correction of the measured cumulants to obtain the
contribution from primary triplets is performed following the decomposition
procedure adopted in [59]. Due to the absence of theoretical and experimental
information on the genuine p–$\Lambda$–K±, p–$\Sigma^{+}$–K± and p–p–$\phi$
correlations, the corresponding feed-down contributions to the p–p–K±
cumulants are considered to be flat, without any specific dependence on
$Q_{3}$. This assumption was tested in the case of p–p–p correlations [59],
where a similar contribution (about 19%) for the $\Lambda$ feed-down into
protons was found. The results obtained by using the flat and non-flat
corrections are found to be in agreement within the uncertainties. A flat
feed-down contribution in the cumulant corresponds to negligible correlations
from genuine three-body effects in the mother particle systems X–Y–Z decaying
into p–p–K±. The dominant feed-down contributions coming from pairwise
correlations in the triplets, such as (X–Y)–Z and X–(Y–Z), are already removed
as they are included in the lower-order correlations. The resulting cumulants
of the correctly identified primary p–p–K+ and p–p–K- particle triplets are
shown in left and right panels of Fig. 3, respectively.

Figure 3: Cumulants for the p–p–K+ (left panel) and p–p–K- (right panel)
primary triplets. The error bars and the blue boxes on the data points
represent the statistical and systematic uncertainties, respectively. The
$n_{\sigma}$ deviations from zero in each bin are shown in the bottom panels,
adding in quadrature the statistical and systematic uncertainties of the data.

In the absence of genuine three-particle correlations in the triplets, the
measured three-particle correlation function would be identical to the lower-
order contributions $C_{3}(Q_{3})=C_{\mathrm{two\text{-}body}}(Q_{3})$.
Consequently, from Eq. (3), the cumulant would be compatible with zero within
the experimental uncertainties. In the case of the p–p–K+ and p–p–K- systems,
the agreement of the measured cumulants with zero is evaluated by performing a
$\chi^{2}$ test in the region $Q_{3}<0.4$ GeV/$c$. In this region, the
relative momentum for all the pairs in the particle triplets is lower than 0.2
GeV/$c$, which is the kinematic region sensitive to the strong interaction.
Larger $Q_{3}$ values are dominated by kinematic configurations in which one
of the pairs has a relative momentum larger than 0.2 GeV/$c$, meaning that
only two of the three particles in the triplet can be in a kinematic
configuration which is favourable for the strong interaction. Thus, at
$Q_{3}>0.4$ GeV/$c$ the contributions from the two-body interaction dominate.
The local statistical significance at $Q_{3}<0.4$ GeV/$c$ is obtained from the
p-value of the $\chi^{2}$ distribution, which is converted in a number
$n_{\sigma}$ of Gaussian standard deviations. As the systematic uncertainties
in different $Q_{3}$ bins are correlated, the $\chi^{2}$ is calculated using
the cumulants extracted from each systematic variation with the corresponding
statistical uncertainties. Finally, the average $\chi^{2}$ in the interval
$Q_{3}<0.4$ GeV/$c$ is extracted. The corresponding $n_{\sigma}$ values are
2.5 and 1.5 for p–p–K+ and p–p–K-, respectively. As the $Q_{3}$ range of the
genuine three-body effects is not known a priori, the $n_{\sigma}$ values have
been evaluated as well in the region $Q_{3}<0.2$ GeV/$c$ and they are found to
be 2.7 and 1.4 for p–p–K+ and p–p–K-, respectively. Such results indicate that
the measured p–p–K+ and p–p–K- correlation functions are compatible, within
the quoted significance levels, with the assumption of pairwise correlations
in the triplets without additional contributions from genuine three-body
effects. More solid conclusions require a larger data sample to reduce the
statistical uncertainties and full-fledged theoretical calculations for the
three-particle correlation functions. In order to provide a comparison of the
kinematic range accessible with this measurement to the study of K+ and K-
interactions in light nuclei, the momenta of the three particles in the
triplets with $Q_{3}<0.4$ GeV/$c$ are evaluated in the PRF of the two protons.
The momentum of each proton in the PRF is $p_{\mathrm{p}}^{*}<180$ MeV/$c$,
compatible with the typical Fermi momenta of nucleons in light nuclei (e.g.
the Fermi momentum in Carbon-12 is about 220 MeV/$c$). In the rest frame of
the two protons, the momentum of the K+ and K- ranges from 30 MeV/$c$ (in the
first bin in Fig. 3) to 130 MeV/$c$ (eighth bin in Fig. 3). This demonstrates
that even for low kaon momenta, i.e. at energies close to the p–p–K+ and
p–p–K- thresholds, three-body effects such as kaonic bound state formation
below threshold or three-body interactions do not contribute significantly to
the measured correlation functions. These results provide additional
experimental information for theoretical models aiming to understand the role
of genuine three-body effects in p–p–K+ and p–p–K- systems. The LHC Run 3 data
taking will deliver a larger data set for more detailed studies of the K+ and
K- three-body dynamics in the low momentum region, down to the energy
threshold of the triplets.

## Acknowledgements

The ALICE Collaboration would like to thank all its engineers and technicians
for their invaluable contributions to the construction of the experiment and
the CERN accelerator teams for the outstanding performance of the LHC complex.
The ALICE Collaboration gratefully acknowledges the resources and support
provided by all Grid centres and the Worldwide LHC Computing Grid (WLCG)
collaboration. The ALICE Collaboration acknowledges the following funding
agencies for their support in building and running the ALICE detector: A. I.
Alikhanyan National Science Laboratory (Yerevan Physics Institute) Foundation
(ANSL), State Committee of Science and World Federation of Scientists (WFS),
Armenia; Austrian Academy of Sciences, Austrian Science Fund (FWF): [M
2467-N36] and Nationalstiftung für Forschung, Technologie und Entwicklung,
Austria; Ministry of Communications and High Technologies, National Nuclear
Research Center, Azerbaijan; Conselho Nacional de Desenvolvimento Científico e
Tecnológico (CNPq), Financiadora de Estudos e Projetos (Finep), Fundação de
Amparo à Pesquisa do Estado de São Paulo (FAPESP) and Universidade Federal do
Rio Grande do Sul (UFRGS), Brazil; Bulgarian Ministry of Education and
Science, within the National Roadmap for Research Infrastructures 2020¿2027
(object CERN), Bulgaria; Ministry of Education of China (MOEC) , Ministry of
Science & Technology of China (MSTC) and National Natural Science Foundation
of China (NSFC), China; Ministry of Science and Education and Croatian Science
Foundation, Croatia; Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear
(CEADEN), Cubaenergía, Cuba; Ministry of Education, Youth and Sports of the
Czech Republic, Czech Republic; The Danish Council for Independent Research —
Natural Sciences, the VILLUM FONDEN and Danish National Research Foundation
(DNRF), Denmark; Helsinki Institute of Physics (HIP), Finland; Commissariat à
l’Energie Atomique (CEA) and Institut National de Physique Nucléaire et de
Physique des Particules (IN2P3) and Centre National de la Recherche
Scientifique (CNRS), France; Bundesministerium für Bildung und Forschung
(BMBF) and GSI Helmholtzzentrum für Schwerionenforschung GmbH, Germany;
General Secretariat for Research and Technology, Ministry of Education,
Research and Religions, Greece; National Research, Development and Innovation
Office, Hungary; Department of Atomic Energy Government of India (DAE),
Department of Science and Technology, Government of India (DST), University
Grants Commission, Government of India (UGC) and Council of Scientific and
Industrial Research (CSIR), India; National Research and Innovation Agency -
BRIN, Indonesia; Istituto Nazionale di Fisica Nucleare (INFN), Italy; Japanese
Ministry of Education, Culture, Sports, Science and Technology (MEXT) and
Japan Society for the Promotion of Science (JSPS) KAKENHI, Japan; Consejo
Nacional de Ciencia (CONACYT) y Tecnología, through Fondo de Cooperación
Internacional en Ciencia y Tecnología (FONCICYT) and Dirección General de
Asuntos del Personal Academico (DGAPA), Mexico; Nederlandse Organisatie voor
Wetenschappelijk Onderzoek (NWO), Netherlands; The Research Council of Norway,
Norway; Commission on Science and Technology for Sustainable Development in
the South (COMSATS), Pakistan; Pontificia Universidad Católica del Perú, Peru;
Ministry of Education and Science, National Science Centre and WUT ID-UB,
Poland; Korea Institute of Science and Technology Information and National
Research Foundation of Korea (NRF), Republic of Korea; Ministry of Education
and Scientific Research, Institute of Atomic Physics, Ministry of Research and
Innovation and Institute of Atomic Physics and University Politehnica of
Bucharest, Romania; Ministry of Education, Science, Research and Sport of the
Slovak Republic, Slovakia; National Research Foundation of South Africa, South
Africa; Swedish Research Council (VR) and Knut & Alice Wallenberg Foundation
(KAW), Sweden; European Organization for Nuclear Research, Switzerland;
Suranaree University of Technology (SUT), National Science and Technology
Development Agency (NSTDA), Thailand Science Research and Innovation (TSRI)
and National Science, Research and Innovation Fund (NSRF), Thailand; Turkish
Energy, Nuclear and Mineral Research Agency (TENMAK), Turkey; National Academy
of Sciences of Ukraine, Ukraine; Science and Technology Facilities Council
(STFC), United Kingdom; National Science Foundation of the United States of
America (NSF) and United States Department of Energy, Office of Nuclear
Physics (DOE NP), United States of America. In addition, individual groups or
members have received support from: European Research Council, Strong 2020 -
Horizon 2020 (grant nos. 950692, 824093), European Union; Academy of Finland
(Center of Excellence in Quark Matter) (grant nos. 346327, 346328), Finland;
Programa de Apoyos para la Superación del Personal Académico, UNAM, Mexico.

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##  Appendix A The ALICE Collaboration

S. Acharya 125, D. Adamová 86, A. Adler69, G. Aglieri Rinella 32, M. Agnello
29, N. Agrawal 50, Z. Ahammed 132, S. Ahmad 15, S.U. Ahn 70, I. Ahuja 37, A.
Akindinov 140, M. Al-Turany 97, D. Aleksandrov 140, B. Alessandro 55, H.M.
Alfanda 6, R. Alfaro Molina 66, B. Ali 15, A. Alici 25, N. Alizadehvandchali
114, A. Alkin 32, J. Alme 20, G. Alocco 51, T. Alt 63, I. Altsybeev 140, M.N.
Anaam 6, C. Andrei 45, A. Andronic 135, V. Anguelov94, F. Antinori 53, P.
Antonioli 50, N. Apadula 74, L. Aphecetche 103, H. Appelshäuser 63, C. Arata
73, S. Arcelli 25, M. Aresti 51, R. Arnaldi 55, J.G.M.C.A. Arneiro 110, I.C.
Arsene 19, M. Arslandok 137, A. Augustinus 32, R. Averbeck 97, M.D. Azmi 15,
A. Badalà 52, J. Bae 104, Y.W. Baek 40, X. Bai 118, R. Bailhache 63, Y.
Bailung 47, A. Balbino 29, A. Baldisseri 128, B. Balis 2, D. Banerjee 4, Z.
Banoo 91, R. Barbera 26, F. Barile 31, L. Barioglio 95, M. Barlou78, G.G.
Barnaföldi 136, L.S. Barnby 85, V. Barret 125, L. Barreto 110, C. Bartels 117,
K. Barth 32, E. Bartsch 63, N. Bastid 125, S. Basu 75, G. Batigne 103, D.
Battistini 95, B. Batyunya 141, D. Bauri46, J.L. Bazo Alba 101, I.G. Bearden
83, C. Beattie 137, P. Becht 97, D. Behera 47, I. Belikov 127, A.D.C. Bell
Hechavarria 135, F. Bellini 25, R. Bellwied 114, S. Belokurova 140, G. Bencedi
136, S. Beole 24, A. Bercuci 45, Y. Berdnikov 140, A. Berdnikova 94, L.
Bergmann 94, M.G. Besoiu 62, L. Betev 32, P.P. Bhaduri 132, A. Bhasin 91, M.A.
Bhat 4, B. Bhattacharjee 41, L. Bianchi 24, N. Bianchi 48, J. Bielčík 35, J.
Bielčíková 86, J. Biernat 107, A.P. Bigot 127, A. Bilandzic 95, G. Biro 136,
S. Biswas 4, N. Bize 103, J.T. Blair 108, D. Blau 140, M.B. Blidaru 97, N.
Bluhme38, C. Blume 63, G. Boca 21,54, F. Bock 87, T. Bodova 20, A.
Bogdanov140, S. Boi 22, J. Bok 57, L. Boldizsár 136, M. Bombara 37, P.M. Bond
32, G. Bonomi 131,54, H. Borel 128, A. Borissov 140, H. Bossi 137, E. Botta
24, Y.E.M. Bouziani 63, L. Bratrud 63, P. Braun-Munzinger 97, M. Bregant 110,
M. Broz 35, G.E. Bruno 96,31, M.D. Buckland 23, D. Budnikov140, H. Buesching
63, S. Bufalino 29, P. Buhler 102, Z. Buthelezi 67,121, A. Bylinkin 20, S.A.
Bysiak107, M. Cai 6, H. Caines 137, A. Caliva 28, E. Calvo Villar 101, J.M.M.
Camacho 109, P. Camerini 23, F.D.M. Canedo 110, M. Carabas 124, A.A. Carballo
32, A.G.B. Carcamo 94, F. Carnesecchi 32, R. Caron 126, L.A.D. Carvalho 110,
J. Castillo Castellanos 128, F. Catalano 32,24, C. Ceballos Sanchez 141, I.
Chakaberia 74, P. Chakraborty 46, S. Chandra 132, S. Chapeland 32, M. Chartier
117, S. Chattopadhyay 132, S. Chattopadhyay 99, T.G. Chavez 44, T. Cheng 97,6,
C. Cheshkov 126, B. Cheynis 126, V. Chibante Barroso 32, D.D. Chinellato 111,
E.S. Chizzali 95, J. Cho 57, S. Cho 57, P. Chochula 32, P. Christakoglou 84,
C.H. Christensen 83, P. Christiansen 75, T. Chujo 123, M. Ciacco 29, C. Cicalo
51, F. Cindolo 50, M.R. Ciupek97, G. ClaiII,50, F. Colamaria 49, J.S.
Colburn100, D. Colella 96,31, M. Colocci 25, G. Conesa Balbastre 73, Z. Conesa
del Valle 72, G. Contin 23, J.G. Contreras 35, M.L. Coquet 128, T.M.
CormierI,87, P. Cortese 130,55, M.R. Cosentino 112, F. Costa 32, S. Costanza
21,54, C. Cot 72, J. Crkovská 94, P. Crochet 125, R. Cruz-Torres 74, P. Cui 6,
A. Dainese 53, M.C. Danisch 94, A. Danu 62, P. Das 80, P. Das 4, S. Das 4,
A.R. Dash 135, S. Dash 46, A. De Caro 28, G. de Cataldo 49, J. de Cuveland38,
A. De Falco 22, D. De Gruttola 28, N. De Marco 55, C. De Martin 23, S. De
Pasquale 28, R. Deb131, S. Deb 47, K.R. Deja133, R. Del Grande 95, L. Dello
Stritto 28, W. Deng 6, P. Dhankher 18, D. Di Bari 31, A. Di Mauro 32, B. Diab
128, R.A. Diaz 141,7, T. Dietel 113, Y. Ding 6, R. Divià 32, D.U. Dixit 18, Ø.
Djuvsland20, U. Dmitrieva 140, A. Dobrin 62, B. Dönigus 63, J.M. Dubinski133,
A. Dubla 97, S. Dudi 90, P. Dupieux 125, M. Durkac106, N. Dzalaiova12, T.M.
Eder 135, R.J. Ehlers 74, F. Eisenhut 63, D. Elia 49, B. Erazmus 103, F.
Ercolessi 25, F. Erhardt 89, M.R. Ersdal20, B. Espagnon 72, G. Eulisse 32, D.
Evans 100, S. Evdokimov 140, L. Fabbietti 95, M. Faggin 27, J. Faivre 73, F.
Fan6, W. Fan 74, A. Fantoni 48, M. Fasel 87, P. Fecchio29, A. Feliciello 55,
G. Feofilov 140, A. Fernández Téllez 44, L. Ferrandi 110, M.B. Ferrer 32, A.
Ferrero 128, C. Ferrero 55, A. Ferretti 24, V.J.G. Feuillard 94, V. Filova35,
D. Finogeev 140, F.M. Fionda 51, F. Flor 114, A.N. Flores 108, S. Foertsch 67,
I. Fokin 94, S. Fokin 140, E. Fragiacomo 56, E. Frajna 136, U. Fuchs 32, N.
Funicello 28, C. Furget73, A. Furs 140, T. Fusayasu 98, J.J. Gaardhøje 83, M.
Gagliardi 24, A.M. Gago 101, C.D. Galvan 109, D.R. Gangadharan 114, P. Ganoti
78, C. Garabatos 97, J.R.A. Garcia44, E. Garcia-Solis 9, C. Gargiulo 32, A.
Garibli81, K. Garner135, P. Gasik 97, A. Gautam 116, M.B. Gay Ducati 65, M.
Germain 103, A. Ghimouz123, C. Ghosh132, M. Giacalone 50,25, P. Giubellino
97,55, P. Giubilato 27, A.M.C. Glaenzer 128, P. Glässel 94, E. Glimos120,
D.J.Q. Goh76, V. Gonzalez 134, S. Gorbunov38, M. Gorgon 2, K. Goswami 47, S.
Gotovac33, V. Grabski 66, L.K. Graczykowski 133, E. Grecka 86, A. Grelli 58,
C. Grigoras 32, V. Grigoriev140, S. Grigoryan 141,1, F. Grosa 32, J.F. Grosse-
Oetringhaus 32, R. Grosso 97, D. Grund 35, G.G. Guardiano 111, R. Guernane 73,
M. Guilbaud 103, K. Gulbrandsen 83, T. Gundem 63, T. Gunji 122, W. Guo 6, A.
Gupta 91, R. Gupta 91, R. Gupta 47, S.P. Guzman44, K. Gwizdziel 133, L. Gyulai
136, M.K. Habib97, C. Hadjidakis 72, F.U. Haider 91, H. Hamagaki 76, A. Hamdi
74, M. Hamid6, Y. Han 138, B.G. Hanley 134, R. Hannigan 108, J. Hansen 75,
M.R. Haque 133, J.W. Harris 137, A. Harton 9, H. Hassan 87, D. Hatzifotiadou
50, P. Hauer 42, L.B. Havener 137, S.T. Heckel 95, E. Hellbär 97, H. Helstrup
34, M. Hemmer 63, T. Herman 35, G. Herrera Corral 8, F. Herrmann135, S.
Herrmann 126, K.F. Hetland 34, B. Heybeck 63, H. Hillemanns 32, B. Hippolyte
127, F.W. Hoffmann 69, B. Hofman 58, B. Hohlweger 84, G.H. Hong 138, M. Horst
95, A. Horzyk 2, Y. Hou6, P. Hristov 32, C. Hughes 120, P. Huhn63, L.M. Huhta
115, T.J. Humanic 88, L.A. Husova 135, A. Hutson 114, R. Ilkaev140, H. Ilyas
13, M. Inaba 123, G.M. Innocenti 32, M. Ippolitov 140, A. Isakov 86, T.
Isidori 116, M.S. Islam 99, M. Ivanov12, M. Ivanov97, V. Ivanov 140, K.E.
Iversen 75, M. Jablonski 2, B. Jacak74, N. Jacazio 25, P.M. Jacobs 74, S.
Jadlovska106, J. Jadlovsky106, S. Jaelani 82, C. Jahnke111, M.J. Jakubowska
133, M.A. Janik 133, T. Janson69, M. Jercic89, S. Ji 16, S. Jia10, A.A.P.
Jimenez 64, F. Jonas 87, J.M. Jowett 32,97, J. Jung 63, M. Jung 63, A. Junique
32, A. Jusko 100, M.J. Kabus 32,133, J. Kaewjai105, P. Kalinak 59, A.S.
Kalteyer 97, A. Kalweit 32, V. Kaplin 140, A. Karasu Uysal 71, D. Karatovic
89, O. Karavichev 140, T. Karavicheva 140, P. Karczmarczyk 133, E. Karpechev
140, U. Kebschull 69, R. Keidel 139, D.L.D. Keijdener58, M. Keil 32, B. Ketzer
42, S.S. Khade 47, A.M. Khan 6, S. Khan 15, A. Khanzadeev 140, Y. Kharlov 140,
A. Khatun 116, A. Khuntia 107, M.B. Kidson113, B. Kileng 34, B. Kim 104, C.
Kim 16, D.J. Kim 115, E.J. Kim 68, J. Kim 138, J.S. Kim 40, J. Kim 57, J. Kim
68, M. Kim 18, S. Kim 17, T. Kim 138, K. Kimura 92, S. Kirsch 63, I. Kisel 38,
S. Kiselev 140, A. Kisiel133, J.P. Kitowski 2, J.L. Klay 5, J. Klein 32, S.
Klein 74, C. Klein-Bösing 135, M. Kleiner 63, T. Klemenz 95, A. Kluge 32, A.G.
Knospe 114, C. Kobdaj 105, T. Kollegger97, A. Kondratyev 141, N. Kondratyeva
140, E. Kondratyuk140, J. Konig 63, S.A. Konigstorfer 95, P.J. Konopka 32, G.
Kornakov 133, S.D. Koryciak 2, A. Kotliarov 86, V. Kovalenko 140, M. Kowalski
107, V. Kozhuharov 36, I. Králik 59, A. Kravčáková 37, L. Krcal 32,38, M.
Krivda 100,59, F. Krizek 86, K. Krizkova Gajdosova 32, M. Kroesen 94, M.
Krüger 63, D.M. Krupova 35, E. Kryshen 140, V. Kučera 57, C. Kuhn 127, P.G.
Kuijer 84, T. Kumaoka123, D. Kumar132, L. Kumar 90, N. Kumar90, S. Kumar 31,
S. Kundu 32, P. Kurashvili 79, A. Kurepin 140, A.B. Kurepin 140, A.
Kuryakin140, S. Kushpil 86, J. Kvapil 100, M.J. Kweon 57, Y. Kwon 138, S.L. La
Pointe 38, P. La Rocca 26, A. Lakrathok105, M. Lamanna 32, R. Langoy 119, P.
Larionov 32, E. Laudi 32, L. Lautner 32,95, R. Lavicka 102, R. Lea 131,54, H.
Lee 104, I. Legrand 45, G. Legras 135, J. Lehrbach 38, T.M. Lelek2, R.C.
Lemmon 85, I. León Monzón 109, M.M. Lesch 95, E.D. Lesser 18, P. Lévai 136, X.
Li10, X.L. Li6, J. Lien 119, R. Lietava 100, I. Likmeta 114, B. Lim 24, S.H.
Lim 16, V. Lindenstruth 38, A. Lindner45, C. Lippmann 97, A. Liu 18, D.H. Liu
6, J. Liu 117, G.S.S. Liveraro111, I.M. Lofnes 20, C. Loizides 87, S. Lokos
107, J. Lomker 58, P. Loncar 33, J.A. Lopez 94, X. Lopez 125, E. López Torres
7, P. Lu 97,118, J.R. Luhder 135, M. Lunardon 27, G. Luparello 56, Y.G. Ma 39,
M. Mager 32, A. Maire 127, M.V. Makariev 36, M. Malaev 140, G. Malfattore 25,
N.M. Malik 91, Q.W. Malik19, S.K. Malik 91, L. Malinina VI,141, D. Mallick 80,
N. Mallick 47, G. Mandaglio 30,52, S.K. Mandal 79, V. Manko 140, F. Manso 125,
V. Manzari 49, Y. Mao 6, R.W. Marcjan 2, G.V. Margagliotti 23, A. Margotti 50,
A. Marín 97, C. Markert 108, P. Martinengo 32, M.I. Martínez 44, G. Martínez
García 103, M.P.P. Martins 110, S. Masciocchi 97, M. Masera 24, A. Masoni 51,
L. Massacrier 72, A. Mastroserio 129,49, O. Matonoha 75, S. Mattiazzo 27,
P.F.T. Matuoka110, A. Matyja 107, C. Mayer 107, A.L. Mazuecos 32, F. Mazzaschi
24, M. Mazzilli 32, J.E. Mdhluli 121, A.F. Mechler63, Y. Melikyan 43,140, A.
Menchaca-Rocha 66, E. Meninno 102,28, A.S. Menon 114, M. Meres 12, S.
Mhlanga113,67, Y. Miake123, L. Micheletti 32, L.C. Migliorin126, D.L. Mihaylov
95, K. Mikhaylov 141,140, A.N. Mishra 136, D. Miśkowiec 97, A. Modak 4, A.P.
Mohanty 58, B. Mohanty 80, M. Mohisin Khan III,15, M.A. Molander 43, Z.
Moravcova 83, C. Mordasini 95, D.A. Moreira De Godoy135, I. Morozov 140, A.
Morsch 32, T. Mrnjavac 32, V. Muccifora 48, S. Muhuri 132, J.D. Mulligan 74,
A. Mulliri22, M.G. Munhoz 110, R.H. Munzer 63, H. Murakami 122, S. Murray 113,
L. Musa 32, J. Musinsky 59, J.W. Myrcha133, B. Naik 121, A.I. Nambrath 18,
B.K. Nandi46, R. Nania 50, E. Nappi 49, A.F. Nassirpour 17,75, A. Nath 94, C.
Nattrass 120, M.N. Naydenov 36, A. Neagu19, A. Negru124, L. Nellen 64, G.
Neskovic 38, B.S. Nielsen 83, E.G. Nielsen 83, S. Nikolaev 140, S. Nikulin
140, V. Nikulin 140, F. Noferini 50, S. Noh 11, P. Nomokonov 141, J. Norman
117, N. Novitzky 123, P. Nowakowski 133, A. Nyanin 140, J. Nystrand 20, M.
Ogino76, A. Ohlson 75, V.A. Okorokov 140, J. Oleniacz 133, A.C. Oliveira Da
Silva 120, M.H. Oliver 137, A. Onnerstad 115, C. Oppedisano 55, A. Ortiz
Velasquez 64, J. Otwinowski 107, M. Oya92, K. Oyama 76, Y. Pachmayer 94, S.
Padhan46, D. Pagano 131,54, G. Paić 64, A. Palasciano 49, S. Panebianco 128,
H. Park 123, H. Park 104, J. Park 57, J.E. Parkkila 32, R.N. Patra91, B. Paul
22, H. Pei 6, T. Peitzmann 58, X. Peng 6, M. Pennisi 24, D. Peresunko 140,
G.M. Perez 7, S. Perrin 128, Y. Pestov140, V. Petrov 140, M. Petrovici 45,
R.P. Pezzi 103,65, S. Piano 56, M. Pikna 12, P. Pillot 103, O. Pinazza 50,32,
L. Pinsky114, C. Pinto 95, S. Pisano 48, M. Płoskoń 74, M. Planinic89, F.
Pliquett63, M.G. Poghosyan 87, B. Polichtchouk 140, S. Politano 29, N. Poljak
89, A. Pop 45, S. Porteboeuf-Houssais 125, V. Pozdniakov 141, I.Y. Pozos44,
K.K. Pradhan 47, S.K. Prasad 4, S. Prasad 47, R. Preghenella 50, F. Prino 55,
C.A. Pruneau 134, I. Pshenichnov 140, M. Puccio 32, S. Pucillo 24, Z.
Pugelova106, S. Qiu 84, L. Quaglia 24, R.E. Quishpe114, S. Ragoni 14, A.
Rakotozafindrabe 128, L. Ramello 130,55, F. Rami 127, S.A.R. Ramirez44, T.A.
Rancien73, M. Rasa 26, S.S. Räsänen 43, R. Rath 50, M.P. Rauch20, I. Ravasenga
84, K.F. Read 87,120, C. Reckziegel112, A.R. Redelbach 38, K. Redlich IV,79,
C.A. Reetz 97, A. Rehman20, F. Reidt 32, H.A. Reme-Ness 34, Z. Rescakova37, K.
Reygers 94, A. Riabov 140, V. Riabov 140, R. Ricci 28, M. Richter19, A.A.
Riedel 95, W. Riegler 32, C. Ristea 62, M.V. Rodriguez 32, M. Rodríguez
Cahuantzi 44, K. Røed 19, R. Rogalev 140, E. Rogochaya 141, T.S. Rogoschinski
63, D. Rohr 32, D. Röhrich 20, P.F. Rojas44, S. Rojas Torres 35, P.S. Rokita
133, G. Romanenko 141, F. Ronchetti 48, A. Rosano 30,52, E.D. Rosas64, K.
Roslon 133, A. Rossi 53, A. Roy 47, S. Roy46, N. Rubini 25, O.V. Rueda 114, D.
Ruggiano133, R. Rui 23, P.G. Russek 2, R. Russo 84, A. Rustamov 81, E.
Ryabinkin 140, Y. Ryabov 140, A. Rybicki 107, H. Rytkonen 115, J. Ryu 16, W.
Rzesa133, O.A.M. Saarimaki 43, R. Sadek 103, S. Sadhu 31, S. Sadovsky 140, J.
Saetre 20, K. Šafařík 35, P. Saha41, S.K. Saha 4, S. Saha 80, B. Sahoo 46, B.
Sahoo 47, R. Sahoo 47, S. Sahoo60, D. Sahu 47, P.K. Sahu 60, J. Saini 132, K.
Sajdakova37, S. Sakai 123, M.P. Salvan 97, S. Sambyal 91, I. Sanna 32,95, T.B.
Saramela110, D. Sarkar 134, N. Sarkar132, P. Sarma41, V. Sarritzu 22, V.M.
Sarti 95, M.H.P. Sas 137, J. Schambach 87, H.S. Scheid 63, C. Schiaua 45, R.
Schicker 94, A. Schmah94, C. Schmidt 97, H.R. Schmidt93, M.O. Schmidt 32, M.
Schmidt93, N.V. Schmidt 87, A.R. Schmier 120, R. Schotter 127, A. Schröter 38,
J. Schukraft 32, K. Schwarz97, K. Schweda 97, G. Scioli 25, E. Scomparin 55,
J.E. Seger 14, Y. Sekiguchi122, D. Sekihata 122, I. Selyuzhenkov 97, S.
Senyukov 127, J.J. Seo 57, D. Serebryakov 140, L. Šerkšnytė 95, A. Sevcenco
62, T.J. Shaba 67, A. Shabetai 103, R. Shahoyan32, A. Shangaraev 140, A.
Sharma90, B. Sharma 91, D. Sharma46, H. Sharma53,107, M. Sharma 91, S. Sharma
76, S. Sharma 91, U. Sharma 91, A. Shatat 72, O. Sheibani114, K. Shigaki 92,
M. Shimomura77, J. Shin11, S. Shirinkin 140, Q. Shou 39, Y. Sibiriak 140, S.
Siddhanta 51, T. Siemiarczuk 79, T.F. Silva 110, D. Silvermyr 75, T.
Simantathammakul105, R. Simeonov 36, B. Singh91, B. Singh 95, K. Singh 47, R.
Singh 80, R. Singh 91, R. Singh 47, S. Singh 15, V.K. Singh 132, V. Singhal
132, T. Sinha 99, B. Sitar 12, M. Sitta 130,55, T.B. Skaali19, G. Skorodumovs
94, M. Slupecki 43, N. Smirnov 137, R.J.M. Snellings 58, E.H. Solheim 19, J.
Song 114, A. Songmoolnak105, C. Sonnabend 32,97, F. Soramel 27, A.B. Soto-
hernandez88, R. Spijkers 84, I. Sputowska 107, J. Staa 75, J. Stachel 94, I.
Stan 62, P.J. Steffanic 120, S.F. Stiefelmaier 94, D. Stocco 103, I. Storehaug
19, P. Stratmann 135, S. Strazzi 25, C.P. Stylianidis84, A.A.P. Suaide 110, C.
Suire 72, M. Sukhanov 140, M. Suljic 32, R. Sultanov 140, V. Sumberia 91, S.
Sumowidagdo 82, S. Swain60, I. Szarka 12, M. Szymkowski133, S.F. Taghavi 95,
G. Taillepied 97, J. Takahashi 111, G.J. Tambave 80, S. Tang 6, Z. Tang 118,
J.D. Tapia Takaki V,116, N. Tapus124, M.G. Tarzila45, G.F. Tassielli 31, A.
Tauro 32, G. Tejeda Muñoz 44, A. Telesca 32, L. Terlizzi 24, C. Terrevoli 114,
S. Thakur 4, D. Thomas 108, A. Tikhonov 140, A.R. Timmins 114, M. Tkacik106,
T. Tkacik 106, A. Toia 63, R. Tokumoto92, N. Topilskaya 140, M. Toppi 48, T.
Tork 72, A.G. Torres Ramos 31, A. Trifiró 30,52, A.S. Triolo 32,30,52, S.
Tripathy 50, T. Tripathy 46, S. Trogolo 32, V. Trubnikov3, W.H. Trzaska 115,
T.P. Trzcinski 133, A. Tumkin140, R. Turrisi 53, T.S. Tveter 19, K. Ullaland
20, B. Ulukutlu 95, A. Uras 126, M. Urioni 54,131, G.L. Usai 22, M. Vala37, N.
Valle 21, L.V.R. van Doremalen58, M. van Leeuwen 84, C.A. van Veen 94, R.J.G.
van Weelden 84, P. Vande Vyvre 32, D. Varga 136, Z. Varga 136, M. Vasileiou
78, A. Vasiliev 140, O. Vázquez Doce 48, V. Vechernin 140, E. Vercellin 24, S.
Vergara Limón44, L. Vermunt 97, R. Vértesi 136, M. Verweij 58, L. Vickovic33,
Z. Vilakazi121, O. Villalobos Baillie 100, A. Villani 23, G. Vino 49, A.
Vinogradov 140, T. Virgili 28, M.M.O. Virta 115, V. Vislavicius75, A.
Vodopyanov 141, B. Volkel 32, M.A. Völkl 94, K. Voloshin140, S.A. Voloshin
134, G. Volpe 31, B. von Haller 32, I. Vorobyev 95, N. Vozniuk 140, J.
Vrláková 37, J. Wan39, C. Wang 39, D. Wang39, Y. Wang 39, A. Wegrzynek 32,
F.T. Weiglhofer38, S.C. Wenzel 32, J.P. Wessels 135, S.L. Weyhmiller 137, J.
Wiechula 63, J. Wikne 19, G. Wilk 79, J. Wilkinson 97, G.A. Willems135, B.
Windelband94, M. Winn 128, J.R. Wright 108, W. Wu39, Y. Wu 118, R. Xu 6, A.
Yadav 42, A.K. Yadav 132, S. Yalcin71, Y. Yamaguchi92, S. Yang20, S. Yano92,
Z. Yin 6, I.-K. Yoo 16, J.H. Yoon 57, H. Yu11, S. Yuan20, A. Yuncu 94, V.
Zaccolo 23, C. Zampolli 32, F. Zanone 94, N. Zardoshti 32, A. Zarochentsev
140, P. Závada 61, N. Zaviyalov140, M. Zhalov 140, B. Zhang 6, L. Zhang 39, S.
Zhang 39, X. Zhang 6, Y. Zhang118, Z. Zhang 6, M. Zhao 10, V. Zherebchevskii
140, Y. Zhi10, D. Zhou 6, Y. Zhou 83, J. Zhu 97,6, Y. Zhu6, S.C. Zugravel 55,
N. Zurlo 131,54

## Affiliation Notes

I Deceased
II Also at: Italian National Agency for New Technologies, Energy and
Sustainable Economic Development (ENEA), Bologna, Italy
III Also at: Department of Applied Physics, Aligarh Muslim University,
Aligarh, India
IV Also at: Institute of Theoretical Physics, University of Wroclaw, Poland
V Also at: University of Kansas, Lawrence, Kansas, United States
VI Also at: An institution covered by a cooperation agreement with CERN

## Collaboration Institutes

1 A.I. Alikhanyan National Science Laboratory (Yerevan Physics Institute)
Foundation, Yerevan, Armenia
2 AGH University of Science and Technology, Cracow, Poland
3 Bogolyubov Institute for Theoretical Physics, National Academy of Sciences
of Ukraine, Kiev, Ukraine
4 Bose Institute, Department of Physics and Centre for Astroparticle Physics
and Space Science (CAPSS), Kolkata, India
5 California Polytechnic State University, San Luis Obispo, California, United
States
6 Central China Normal University, Wuhan, China
7 Centro de Aplicaciones Tecnológicas y Desarrollo Nuclear (CEADEN), Havana,
Cuba
8 Centro de Investigación y de Estudios Avanzados (CINVESTAV), Mexico City and
Mérida, Mexico
9 Chicago State University, Chicago, Illinois, United States
10 China Institute of Atomic Energy, Beijing, China
11 Chungbuk National University, Cheongju, Republic of Korea
12 Comenius University Bratislava, Faculty of Mathematics, Physics and
Informatics, Bratislava, Slovak Republic
13 COMSATS University Islamabad, Islamabad, Pakistan
14 Creighton University, Omaha, Nebraska, United States
15 Department of Physics, Aligarh Muslim University, Aligarh, India
16 Department of Physics, Pusan National University, Pusan, Republic of Korea
17 Department of Physics, Sejong University, Seoul, Republic of Korea
18 Department of Physics, University of California, Berkeley, California,
United States
19 Department of Physics, University of Oslo, Oslo, Norway
20 Department of Physics and Technology, University of Bergen, Bergen, Norway
21 Dipartimento di Fisica, Università di Pavia, Pavia, Italy
22 Dipartimento di Fisica dell’Università and Sezione INFN, Cagliari, Italy
23 Dipartimento di Fisica dell’Università and Sezione INFN, Trieste, Italy
24 Dipartimento di Fisica dell’Università and Sezione INFN, Turin, Italy
25 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Bologna, Italy
26 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Catania, Italy
27 Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN,
Padova, Italy
28 Dipartimento di Fisica ‘E.R. Caianiello’ dell’Università and Gruppo
Collegato INFN, Salerno, Italy
29 Dipartimento DISAT del Politecnico and Sezione INFN, Turin, Italy
30 Dipartimento di Scienze MIFT, Università di Messina, Messina, Italy
31 Dipartimento Interateneo di Fisica ‘M. Merlin’ and Sezione INFN, Bari,
Italy
32 European Organization for Nuclear Research (CERN), Geneva, Switzerland
33 Faculty of Electrical Engineering, Mechanical Engineering and Naval
Architecture, University of Split, Split, Croatia
34 Faculty of Engineering and Science, Western Norway University of Applied
Sciences, Bergen, Norway
35 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical
University in Prague, Prague, Czech Republic
36 Faculty of Physics, Sofia University, Sofia, Bulgaria
37 Faculty of Science, P.J. Šafárik University, Košice, Slovak Republic
38 Frankfurt Institute for Advanced Studies, Johann Wolfgang Goethe-
Universität Frankfurt, Frankfurt, Germany
39 Fudan University, Shanghai, China
40 Gangneung-Wonju National University, Gangneung, Republic of Korea
41 Gauhati University, Department of Physics, Guwahati, India
42 Helmholtz-Institut für Strahlen- und Kernphysik, Rheinische Friedrich-
Wilhelms-Universität Bonn, Bonn, Germany
43 Helsinki Institute of Physics (HIP), Helsinki, Finland
44 High Energy Physics Group, Universidad Autónoma de Puebla, Puebla, Mexico
45 Horia Hulubei National Institute of Physics and Nuclear Engineering,
Bucharest, Romania
46 Indian Institute of Technology Bombay (IIT), Mumbai, India
47 Indian Institute of Technology Indore, Indore, India
48 INFN, Laboratori Nazionali di Frascati, Frascati, Italy
49 INFN, Sezione di Bari, Bari, Italy
50 INFN, Sezione di Bologna, Bologna, Italy
51 INFN, Sezione di Cagliari, Cagliari, Italy
52 INFN, Sezione di Catania, Catania, Italy
53 INFN, Sezione di Padova, Padova, Italy
54 INFN, Sezione di Pavia, Pavia, Italy
55 INFN, Sezione di Torino, Turin, Italy
56 INFN, Sezione di Trieste, Trieste, Italy
57 Inha University, Incheon, Republic of Korea
58 Institute for Gravitational and Subatomic Physics (GRASP), Utrecht
University/Nikhef, Utrecht, Netherlands
59 Institute of Experimental Physics, Slovak Academy of Sciences, Košice,
Slovak Republic
60 Institute of Physics, Homi Bhabha National Institute, Bhubaneswar, India
61 Institute of Physics of the Czech Academy of Sciences, Prague, Czech
Republic
62 Institute of Space Science (ISS), Bucharest, Romania
63 Institut für Kernphysik, Johann Wolfgang Goethe-Universität Frankfurt,
Frankfurt, Germany
64 Instituto de Ciencias Nucleares, Universidad Nacional Autónoma de México,
Mexico City, Mexico
65 Instituto de Física, Universidade Federal do Rio Grande do Sul (UFRGS),
Porto Alegre, Brazil
66 Instituto de Física, Universidad Nacional Autónoma de México, Mexico City,
Mexico
67 iThemba LABS, National Research Foundation, Somerset West, South Africa
68 Jeonbuk National University, Jeonju, Republic of Korea
69 Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik,
Fachbereich Informatik und Mathematik, Frankfurt, Germany
70 Korea Institute of Science and Technology Information, Daejeon, Republic of
Korea
71 KTO Karatay University, Konya, Turkey
72 Laboratoire de Physique des 2 Infinis, Irène Joliot-Curie, Orsay, France
73 Laboratoire de Physique Subatomique et de Cosmologie, Université Grenoble-
Alpes, CNRS-IN2P3, Grenoble, France
74 Lawrence Berkeley National Laboratory, Berkeley, California, United States
75 Lund University Department of Physics, Division of Particle Physics, Lund,
Sweden
76 Nagasaki Institute of Applied Science, Nagasaki, Japan
77 Nara Women’s University (NWU), Nara, Japan
78 National and Kapodistrian University of Athens, School of Science,
Department of Physics , Athens, Greece
79 National Centre for Nuclear Research, Warsaw, Poland
80 National Institute of Science Education and Research, Homi Bhabha National
Institute, Jatni, India
81 National Nuclear Research Center, Baku, Azerbaijan
82 National Research and Innovation Agency - BRIN, Jakarta, Indonesia
83 Niels Bohr Institute, University of Copenhagen, Copenhagen, Denmark
84 Nikhef, National institute for subatomic physics, Amsterdam, Netherlands
85 Nuclear Physics Group, STFC Daresbury Laboratory, Daresbury, United Kingdom
86 Nuclear Physics Institute of the Czech Academy of Sciences, Husinec-Řež,
Czech Republic
87 Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States
88 Ohio State University, Columbus, Ohio, United States
89 Physics department, Faculty of science, University of Zagreb, Zagreb,
Croatia
90 Physics Department, Panjab University, Chandigarh, India
91 Physics Department, University of Jammu, Jammu, India
92 Physics Program and International Institute for Sustainability with Knotted
Chiral Meta Matter (SKCM2), Hiroshima University, Hiroshima, Japan
93 Physikalisches Institut, Eberhard-Karls-Universität Tübingen, Tübingen,
Germany
94 Physikalisches Institut, Ruprecht-Karls-Universität Heidelberg, Heidelberg,
Germany
95 Physik Department, Technische Universität München, Munich, Germany
96 Politecnico di Bari and Sezione INFN, Bari, Italy
97 Research Division and ExtreMe Matter Institute EMMI, GSI Helmholtzzentrum
für Schwerionenforschung GmbH, Darmstadt, Germany
98 Saga University, Saga, Japan
99 Saha Institute of Nuclear Physics, Homi Bhabha National Institute, Kolkata,
India
100 School of Physics and Astronomy, University of Birmingham, Birmingham,
United Kingdom
101 Sección Física, Departamento de Ciencias, Pontificia Universidad Católica
del Perú, Lima, Peru
102 Stefan Meyer Institut für Subatomare Physik (SMI), Vienna, Austria
103 SUBATECH, IMT Atlantique, Nantes Université, CNRS-IN2P3, Nantes, France
104 Sungkyunkwan University, Suwon City, Republic of Korea
105 Suranaree University of Technology, Nakhon Ratchasima, Thailand
106 Technical University of Košice, Košice, Slovak Republic
107 The Henryk Niewodniczanski Institute of Nuclear Physics, Polish Academy of
Sciences, Cracow, Poland
108 The University of Texas at Austin, Austin, Texas, United States
109 Universidad Autónoma de Sinaloa, Culiacán, Mexico
110 Universidade de São Paulo (USP), São Paulo, Brazil
111 Universidade Estadual de Campinas (UNICAMP), Campinas, Brazil
112 Universidade Federal do ABC, Santo Andre, Brazil
113 University of Cape Town, Cape Town, South Africa
114 University of Houston, Houston, Texas, United States
115 University of Jyväskylä, Jyväskylä, Finland
116 University of Kansas, Lawrence, Kansas, United States
117 University of Liverpool, Liverpool, United Kingdom
118 University of Science and Technology of China, Hefei, China
119 University of South-Eastern Norway, Kongsberg, Norway
120 University of Tennessee, Knoxville, Tennessee, United States
121 University of the Witwatersrand, Johannesburg, South Africa
122 University of Tokyo, Tokyo, Japan
123 University of Tsukuba, Tsukuba, Japan
124 University Politehnica of Bucharest, Bucharest, Romania
125 Université Clermont Auvergne, CNRS/IN2P3, LPC, Clermont-Ferrand, France
126 Université de Lyon, CNRS/IN2P3, Institut de Physique des 2 Infinis de
Lyon, Lyon, France
127 Université de Strasbourg, CNRS, IPHC UMR 7178, F-67000 Strasbourg, France,
Strasbourg, France
128 Université Paris-Saclay Centre d’Etudes de Saclay (CEA), IRFU, Départment
de Physique Nucléaire (DPhN), Saclay, France
129 Università degli Studi di Foggia, Foggia, Italy
130 Università del Piemonte Orientale, Vercelli, Italy
131 Università di Brescia, Brescia, Italy
132 Variable Energy Cyclotron Centre, Homi Bhabha National Institute, Kolkata,
India
133 Warsaw University of Technology, Warsaw, Poland
134 Wayne State University, Detroit, Michigan, United States
135 Westfälische Wilhelms-Universität Münster, Institut für Kernphysik,
Münster, Germany
136 Wigner Research Centre for Physics, Budapest, Hungary
137 Yale University, New Haven, Connecticut, United States
138 Yonsei University, Seoul, Republic of Korea
139 Zentrum für Technologie und Transfer (ZTT), Worms, Germany
140 Affiliated with an institute covered by a cooperation agreement with CERN
141 Affiliated with an international laboratory covered by a cooperation
agreement with CERN.


# BON: An extended public domain dataset for human activity recognition

Girmaw Abebe Tadesse  Oliver Bent  Komminist Weldemariam  Md. Abrar Istiak
Taufiq Hasan  Andrea Cavallaro

###### Abstract

Body-worn first-person vision (FPV) camera enables to extract a rich source of
information on the environment from the subject’s viewpoint. However, the
research progress in wearable camera-based egocentric office activity
understanding is slow compared to other activity environments (e.g., kitchen
and outdoor ambulatory), mainly due to the lack of adequate datasets to train
more sophisticated (e.g., deep learning) models for human activity recognition
in office environments. This paper provides details of a large and publicly
available office activity dataset (BON) collected in different office settings
across three geographical locations: Barcelona (Spain), Oxford (UK) and
Nairobi (Kenya), using a chest-mounted GoPro Hero camera. The BON dataset
contains eighteen common office activities that can be categorised into
person-to-person interactions (e.g., Chat with colleagues), person-to-object
(e.g., Writing on a whiteboard), and proprioceptive (e.g., Walking).
Annotation is provided for each segment of video with 5-seconds duration.
Generally, BON contains 25 subjects and 2639 total segments. In order to
facilitate further research in the sub-domain, we have also provided results
that could be used as baselines for future studies.

###### Keywords:

First-person vision, , Human activity recognition, Privacy, Dataset

##  1 Introduction

Human activity recognition (HAR) is one of the well-explored domains but still
requires further studies due to its challenges, mainly associated with the
variability in: sensors (e.g., inertial measurement units and camera),
subjects (e.g., gender, age and gait), activities (e.g., ambulatory and
interactive) and environments (e.g., indoor and outdoor). HAR plays a crucial
role in the fields of human-computer interactions, assistive computer vision
and robotics. Particularly, activity recognition in workplaces (aka office
activity recognition) includes personal activity tracking of a subject that
could be utilised to maximise productivity and safety.

Various modalities have been employed to encode information for the task of
human activity recognition. These include inertial measurement units (e.g.,
accelerometer) [1, 2, 3, 4], radar [5, 6], WiFi [6] signals, RGB depth video
[7], infrared [8, 9], skeleton [10] and point cloud [11] data.

The development of cheaper and easy-to-use cameras has made huge progress in
collecting more image/video data, which provide richer information in both
spatial and temporal dimensions while privacy and environmental sensitivity
could be an issue. Depending on the topology between camera used and active
subject under consideration (for which the information is collected for),
visual data could be collected from two perspectives: traditional (third-
person vision) and egocentric (first-person vision). In a complex and highly
dynamic environment where the density of objects is high, a third-person
camera provides a global view of the high-level attributes in a scene and
preferred to understand group-based human activities but stills susceptible to
privacy issues as the active user is clearly seen in this setting. On the
other hand, first-person camera can capture finer details about objects and
people with superior level of granularity from the user’s field of view while
privacy is relatively protected as the active user will not be seen in the
video collected (see Fig. 1).

Figure 1: Chest-mounted wearable camera setting, used for collecting the BON
dataset, and an example of a first-person video (FPV) frame from the dataset.

Understanding of egocentric human activities is crucial across different
application domains. A significant research progress has been made in recent
years to identify kitchen [12, 13] or outdoor [14, 15, 16, 17] activities.
However, the research progress in wearable camera-based egocentric office
activity understanding is still minimal, partly due to the lack of adequate
datasets to train more sophisticated (e.g., deep learning) [18, 19]. Wojek et
al. [20] reported one of the earlier works in office activity understanding
that employed fusion of audio and (third-person) video data using hidden
Markov models (HMMs). The set of studied activities in this work includes
meetings, paperwork, phone calls, and nobody present. Ying et al. [21]
detected abnormal activities in office setting by utilising Radon transform on
binary human silhouette. This has been later extended to encompass twelve
office activities using hand posture cues in [22]. Tadesse et al. [19]
extended the number office activities to eighteen but using FPV videos
collected from 12 subjects in unique office setting. Recognition of office
activities have also been reported using sensors other than wearable camera,
such as accelerometer [23], passive infrared, pressure and acoustic sensors
[24], gyroscope [25].

In this paper we present BON - a large and publicly available FPV dataset of
office activities, which was extended from [19] by diversifying the data
collection settings. BON is available at [26] and it consists of $2639$ video
segments. First, we present the data collection and annotation set-up followed
by critical challenges associated with encoding human activities from first-
person videos of users (Section 2). The BON dataset was previously utilized
for Video and Image Processing (VIP) Cup 2019 challenge [27], and baseline
results from the challenge are also provided in Section 3 so that future works
could compare against these baselines. Finally, Section 4 concludes the paper.

##  2 BON: Large scale office activities dataset

BON represents a new FPV dataset of office activities collected in Barcelona
(Spain), Oxford (UK) and Nairobi (Kenya) using a GoPro Hero wearable camera
[26]. BON is extended from [19] by diversifying the collection settings across
different countries along with increased subjects’ participation. Multiple
office settings are included in BON dataset, which will help to develop
generalisable HAR models that are robust against variations. In this Section,
we describe the details of BON including data collection setting, definition
of activities, segments stratification (e.g., across locations). We have also
highlighted technical challenges available in the BON dataset (e.g., specific
inter-class similarity and intra-class variations in office settings).

###  2.1 Data collection

A chest-mounted GoPro Hero3+ camera was used to collect video segments of the
BON dataset with a resolution of $1280\times 720$ and a rate of $30$ frames
per second. Participants were instructed to record themselves (i.e., to press
the recording button) while they were performing common office activities. In
total there are $18$ activities in BON, which can be categorized into three
categories: Person-to-object interactions, Person-to-person interactions, and
proprioceptive activity. A descriptive definition of all activities is given
in Table 1. Randomly selected samples from each activity are also shown in
Fig. 2.

A total of $25$ subjects were participated in the collection of BON: $12$
subjects in Barcelona, $7$ subjects in Oxford, and $6$ subjects in Nairobi
sub-datasets. A quantitative description is given in Table 2. Each subject
recorded a continuous video sequence. Then a pre-processing is employed to
segment a continuous recording of activity into a video sample of 5-seconds
duration. This results in a total of $2639$ video segments, and the
distribution of these segments across the three locations is presented in
Table 3. When stratified across locations, $1308$ segments were collected in
Oxford, $464$ segments in Barcelona and $867$ segments Nairobi. Among
activities shake, staple, and take represented the minority groups.

Table 1: Definition of activities in the BON dataset

Label | Definition
---|---
Chat | Chatting with other person
Clean | Cleaning the whiteboard using a duster or an ink remover
Drink | Drinking from a can or a cup or bottle or glass
Dry | Drying hand using electric hand dryer
Machine | Placing an order in a vending machine
Microwave | Heating food using a microwave
Mobile | Scrolling mobile screen
Paper | Reading from a paper
Print | Taking out printed paper from a printer or xerox machine
Read | Reading from a computer screen
Shake | Shaking hand with another person
Staple | Stapling papers using a stapler
Take | Taking out a bottle or a can or a container from a vending machine
Typeset | Typing computer keyboard
Walk | Walking naturally
Wash | Washing hand in a sink
Whiteboard | Writing on whiteboard using a marker
Write | Writing on paper using a pen or a pencil

Table 2: Stratification of collected video segments across locations.

|  | Sex |
---|---|---|---
Location | Num. of Subjects | Male | Female | Num. of Segments
Barcelona | $12$ | $9$ | $3$ | $464$
Oxford | $7$ | 5 | 2 | $1308$
Nairobi | $6$ | 4 | 2 | $867$
Total | $25$ | 18 | 7 | 2639

Table 3: Distribution of office activities across the three geographical
locations in the BON dataset.

| Location |
---|---|---
Label | Barcelona | Nairobi | Oxford | Total
Chat | 30 | 61 | 99 | 190
Clean | 19 | 44 | 54 | 117
Drink | 22 | 53 | 69 | 144
Dry | 16 | 24 | 61 | 101
Machine | 21 | 42 | 76 | 139
Microwave | 27 | 60 | 93 | 180
Mobile | 13 | 46 | 74 | 133
Paper | 22 | 45 | 92 | 159
Print | 17 | 51 | 53 | 121
Read | 20 | 52 | 79 | 151
Shake | 17 | 18 | 34 | 69
Staple | 16 | 30 | 49 | 95
Take | 21 | 30 | 44 | 95
Typeset | 26 | 75 | 111 | 212
Walk | 90 | 73 | 74 | 237
Wash | 22 | 31 | 60 | 113
Whiteboard | 47 | 79 | 97 | 223
Write | 18 | 53 | 89 | 160
Total | 464 | 867 | 1308 | 2639

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

(i)

(j)

(k)

(l)

(m)

(n)

(o)

(p)

(q)

(r)

Figure 2: Key frames from sampled videos of $18$ activities in the BON dataset
that can be grouped into three categories. (a) - (o) person-to-object
interaction category, (p) proprioceptive category, and (q) - (r) person-to-
person category.

###  2.2 Annotation

The dataset is provided with proper annotation for each 5-seconds video
segment. It enables supervised learning in the activity recognition task. ELAN
[28] tool and external camera was used to annotate video segments from the
Barcelona sub-dataset. For video segments collected in Nairobi and Oxford, we
followed concurrent voice annotation by having the user calling out the
activity name at the start of each activity. Finally, the preprocessing is
performed to segment out each 5-seconds duration and where the audio is muted.

###  2.3 Challenges

The field of computer vision is known for challenges, such as illumination
changes, occlusion, outlier motion, and privacy, which are also reflected in
the BON dataset (see Fig. 3). For example, changes in illumination changes,
caused by unanticipated brightness variations (Fig. 3(a) and 3(b)), were
observed due to the mixture of indoor and outdoor lighting. Occlusion, on the
other hand, can partially or completely overshadows the object of interest in
the video. Example is a camera occluded by moving hands as in Fig. 3(c). As
the FPV camera is chest-mounted, movement of human body could result in
outlier motion in the video scene and/or blurry frames (see Fig. 3(d) and
3(e)). Another concerning issue is privacy. For instance, chatting with
someone (Fig. 3(g)) could reveal the faces of many individuals indirectly;
moreover, sensitive content could also be exposed while working on a computer
or accessing a mobile phone. Privacy-preserving in first-person videos is a
growing research interest [27], and BON could provide more data to further
strengthen this research.

(a)

(b)

(c)

(d)

(e)

(f)

(g)

(h)

Figure 3: Examples of video frames to demonstrate some of the challenges in
the BON dataset: illumination changes in _mobile_ in (a) and _shake_ in (b)
activities. Other challenges include occlusion in _chat_ activity in (c), some
frames fade due to the motion of the subject in (d), outlier motion happens
while _stapling_ ends in (e), outlier motion occurs while _shaking_ hand in
(f), and privacy exposed in _chat_ and _mobile_ activities in (g) and (h),
respectively, as faces of associated people and mobile password is disclosed
in the frame.

Additional challenges evident in the BON dataset include intra-class
variations (e.g, how different subjects perform similar activity differently)
and inter-class similarity (e.g., how different activities could appear
similar in first-person videos) as shown in Figure 4. Further examples are
also provided in Fig. 5 to demonstrate intra-class variations due to different
office settings, e.g., different hand-drying machines. Examples for inter-
class similarity are shown between _Read_ (in Fig. 6(c)) and _Typeset_ (in
Fig. 6(d)). Similarly, Fig. 6(a) and Fig. 6(b) for _Machine_ and _Take_ ,
respectively, have shown resemblance in how the user interacts with a vending
machine To build a robust classifier, indistinguishable activities like these
activities need more attention.

(a)

(b)

Figure 4: Examples of intra-class variations where an activity is performed
differently across subjects: (a) Microwave activity and (b) Machine activity.

(a)

(b)

(c)

(d)

(e)

(f)

Figure 5: Variations in office settings: the first column shows different hand
drying machines and the second column shows indoor walking environments across
the three locations used for data collections: Barcelona, Oxford and Nairobi.

(a)

(b)

(c)

(d)

Figure 6: Inter-activity similarity. Comparison with the first column to the
second column illustrates the similarity between two different activities, top
row: Machine vs. Take, bottom row: Read vs. Typeset.

##  3 Preliminary experiments

BON dataset was used in a Video and Image Processing (VIP) Cup - a challenge
organised by IEEE Signal Processing Society (SPC) at at the IEEE International
Conference on Image Processing, 2019, Taiwan [27]. The main task of the
challenge was to recognize activities. For training, whole video segments from
Barcelona sub-dataset and the segments from the first two subjects in Oxford
and Nairobi were used, whereas the remaining video segments from Oxford and
Nairobi sub-datasets were used for testing. To this end, top-3 (based on
average $F_{1}$ score) performing activity recognition frameworks were
selected (see Table 4) and described below. These frameworks could be utilized
as baseline for future research.

Among the proposed frameworks in the IEEE VIP Cup 2019, the highest scoring
baseline model used a spatial temporal attention reasoning (STANet) [29] for
activity recognition, whereas features were extracted from both the RGB and
corresponding optical flow frames using ResNet-34 [30]. Long Short-term Memory
(LSTM) network [31] was employed, followed by spatial attention [32], to
encode spatio-temporal information. The second ranked framework was an end-to-
end deep learning approach which employs Inception-V3 [33] to extract features
from the video frames, which were later fed into a multilayer perceptron (MLP)
[34] for a final prediction. The third ranked framework was based on ensemble
of several Recurrent Neural Networks (RNNs) with framewise attention [35].
Different models pre-trained on ImageNet [36] (e.g., DenseNet [37], Wide-
ResNet [38]) were used for extracting features from sampled video frames. The
ensemble scheme was based on $F_{1}$ score for particular classes of different
models trained on different resolutions. Additionally, to handle class
imbalance in the dataset, a balanced batch technique was employed. Categorical
cross entropy is used as loss and initial layers of feature extractors were
frozen during the transfer learning from ImageNet pre-trained models [35].

Table 4: Comparison of baseline systems Method | Avg F1 score
---|---
STANet [29] | 0.749
MLP [34] | 0.678
RNN Ensemble [35] | 0.658

##  4 Conclusion

Egocentric vision is a growing field of research in computer vision, and
unlike other sub-domains (e.g., kitchen activities [13]), there is substantial
lack of data in understanding human activities from using egocentric vision at
work places or in office environments. This paper presents a large and
publicly available dataset is aimed at releasing the BON dataset - collected
using a chest mounted GoPro Hero3+ camera for eighteen office activities [26].
The BON dataset is extended from [19], by including diversified office
settings across three countries and increased number of participants resulting
$2639$ labeled video segments collected from 25 participants. Moreover, this
paper describes the details of the dataset (and its video segments) stratified
across locations, subjects and activities. In addition, BON was shown to
include real world challenges in computer vision, such as illumination
changes, occlusion, intra-class variations and inter-class similarity. As
baselines for future research, we also shared three activity recognition
frameworks that topped the ranking in the previous IEEE VIP Cup 2019, where
the full version of BON dataset was first utilized. We hope that this publicly
available dataset helps to facilitate the research efforts in the field of
egocentric activity recognition from wearable cameras, particularly
recognition of activities in office settings that provides a promising
potential in human-computer interactions, security and product monitoring.

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