# All-Day Object Tracking for Unmanned Aerial Vehicle

Bowen Li, Changhong Fu*, Fangqiang Ding,
Junjie Ye, and Fuling Lin,  *Corresponding author The aurhors are with the
School of Mechanical Engineering, Tongji University, 201804 Shanghai, China.
(Email<EMAIL_ADDRESS>
###### Abstract

Visual object tracking, which is representing a major interest in image
processing field, has facilitated numerous real-world applications. Among
them, equipping unmanned aerial vehicle (UAV) with real-time robust visual
trackers for all-day aerial maneuver, is currently attracting incremental
attention and has remarkably broadened the scope of applications of object
tracking. However, prior tracking methods have merely focused on robust
tracking in the well-illuminated scenes, while ignoring trackers’ capabilities
to be deployed in the dark. In darkness, the conditions can be more complex
and harsh, easily posing inferior robust tracking or even tracking failure. To
this end, this work proposed a novel discriminative correlation filter-based
tracker with illumination adaptive and anti-dark capability, namely ADTrack.
ADTrack firstly exploits image illuminance information to enable adaptability
of the model to the given light condition. Then, by virtue of an efficient and
effective image enhancer, ADTrack carries out image pretreatment, where a
target-aware mask is generated. Benefiting from the mask, ADTrack aims to
solve a dual regression problem where dual filters, i.e., the context filter
and target-focused filter, are trained with mutual constraint. Thus ADTrack is
able to maintain continuously favorable performance in all-day conditions.
Besides, this work also constructed one UAV nighttime tracking benchmark
UAVDark135, comprising of more than 125k manually annotated frames, which is
also very first UAV dark tracking benchmark. Exhaustive experiments are
extended on authoritative daytime benchmarks, i.e., UAV123@10fps, DTB70, and
the newly built dark benchmark UAVDark135. Our results have validated the
superiority of ADTrack in both bright and dark conditions compared with other
arts. Meanwhile, ADTrack realizes a real-time speed of over 30 frames/s on a
single CPU, remarkably ensuring real-world UAV object tracking under all-day
scenes.

###### Index Terms:

Unmanned aerial vehicle, visual object tracking, discriminative correlation
filter, dark tracking benchmark, image illumination based mask, dual
regression model.

##  I Introduction

Standing as one of the hotspots in image processing field, visual object
tracking aims at estimating the location and scale of an initial given object
in the subsequent frames of an image sequence. Applying such flourishing
approach onboard unmanned aerial vehicle (UAV) has enabled extensive
applications in practice, e.g., path optimization and planning [1], disaster
response [2], target following [3], and autonomous landing [4]. Specifically,
transmission-line inspection [5], collision avoidance [6], and aircraft
tracking [7], often need around-the-clock operation.

Figure 1: Performance comparison of SOTA trackers on the newly constructed
UAVDark135, where tracking scenes are generally much darker and more onerous.
Clearly, ADTrack outperforms the other trackers in both distance precision
(DP), and area under curve (AUC), maintaining favorable robustness even in
harsh dark conditions. Figure 2: The first frames of representative scenes in
newly constructed UAVDark135. Here, target ground-truths are marked out by
green boxes and sequence names are located at the top left corner of the
images. Dark special challenges like objects’ unreliable color feature and
objects’ merging into the dark can be seen clearly.

Unfortunately, state-of-the-art (SOTA) trackers [8, 9, 10, 11, 12, 13, 14, 15,
16] only focus on tracking in bright environment, where external illumination
condition is favorable and the inline texture as well as outline of the object
is representatvie. When the night falls, despite that the content of the scene
is discernible, the visual quality of images captured is barely satisfactory,
hurting the performance of methods that are primarily designed for object
tracking with high-visibility inputs. Compared with common tracking scenes,
tracking in the dark onboard UAV confronts special undesirable hardships such
as:

  * •

Color distortion: Since the objects in the dark are not well-illuminated, very
little light is reflected on their surfaces, making them nearly all-dark. In
this case, the objects’ color is distorted. Without representative color
features, the discriminative ability of the tracker can decrease in a notable
margin.

  * •

Low visibility: When the object enters dimmer region in the dark, like the
shadow, it can merge into the dark background, ending up in low visibility.
Such scenes set barrier to trackers’ robust localization and precise scale
estimation of the object.

  * •

High-level noise: Images captured by UAV at night inevitably contains random
noise, which may distract trackers, resulting in inferior robust tracking.

  * •

Limited computation: For cost issue and scarce battery power, UAV generally
carries a single CPU as its computation platform. While in order to handle
tough darkness, more modules are needed to maintain tracking robustness,
making real-time processing even more arduous.

Though the outstanding trackers [17, 8, 9, 10, 11, 18, 19, 14, 15, 16] have
achieved promising performance in well-illuminated scenes. The deep trackers
[18, 19, 14, 15, 16] on the one hand introduce too much computation burden to
be deployed on a single CPU for real-time tracking. On the other, the
discriminative ability of deep semantic features were proved to drop
significantly in our experiment since the off-the-shelf network is trained by
bright images. While the brilliant discriminative correlation filter
(CF)-based trackers [17, 8, 9, 10, 11], which utilize online learned
handcrafted features, easily lose accuracy and robustness under low-light
nighttime scenes due to the challenges mentioned above. In Fig. 1, both deep
and handcrafted CF-based methods fails to meet our expectation. Prior work
contributed very few to robust tracking in the dark, while there is an urgent
need to develop to broaden the service life and usage scenarios of UAV.

In this regard, this work proposes a novel and pioneer tracker with
illuminance adaptive and anti-dark function (ADTrack) to achieve robust all-
day real-time UAV tracking. Fig. 1 exhibits the superiority of proposed
ADTrack against other arts in nighttime UAV tracking scene.

To be specific, ADTrack explores image illumination processing methods [20,
21] and proposes more innovative module to be embedded into efficient robust
CF-based tracker [10]. To achieve robust tracking under 2 distinct light
condition, i.e., day and night, ADTrack firstly exploits image illumination
[20] to detect and adapt to the condition, which is inspired by human visual
system and proves to be effective and efficient. Then, a fast enhancer [21]
generates appropriate image samples according to the detection result for
training.

Surprisingly, we found the image illumination map in [21] can be utilized to
obtain a target-aware mask. By virtue of the mask, ADTrack solves a dual
regression model to train target-focused and context filters with mutual
constraint. During detection phase, dual responses, i.e., target-focused and
context response maps, are fused using weight sum to achieve more precise
object localization. With the proposed dual filters, ADTrack proves to be more
robust in around-the-clock UAV tracking scenes.

Besides, to the best of our knowledge, there exists no dark UAV tracking
benchmark for large-scale evaluation in literature. Thus, this work also
builds the very first UAV dark tracking benchmark, i.e., UAVDark135.
UAVDark135 consists of totally 135 sequences, most of which were newly shot by
a standard UAV at night, including more than 125k manually annotated frames.
The benchmark covers a wide range of scenes, e.g., road, ocean, street,
highway, and lakeside, including a large number of objects, such as person,
car, building, athlete, truck, and bike. Fig. 2 displays representative scenes
in UAVDark135, where dark special challenges are distinct.

Contributions111The source code of the proposed ADTrack and newly constructed
benchmark UAVDark135 are located at
https://github.com/vision4robotics/ADTrack_v2. of this work can be summarized
as:

  * •

This work proposes a novel tracker with illumination adaptive and anti-dark
function (ADTrack).

  * •

The proposed ADTrack exploits image illumination to acquire target-aware mask,
which can be creatively utilized to solve a dual regression.

  * •

This work constructed the very pioneer UAV dark tracking benchmark UAVDark135
to perform large-scale evaluation.

  * •

Exhaustive experiments have been conducted on two authoritative daytime
benchmark UAV123@10fps, DTB70 and the newly built nighttime benchmark
UAVDark135 to validate the surprising ability of proposed ADTrack in around-
the-clock tracking performance.

The remainder of this work is organized as follows. Section II summarizes
related work about image enhancing approaches, CF-based tracking methods, and
target-aware tracking approaches. Section III elaborately interprets the
proposed ADTrack, which can be epitomized as 4 modules, respectively,
illumination adaptation, pretreatment, filter training, and object detection.
Section IV gives a thorough introduction of the newly built UAVDark135,
including its statistics, platform, annotation, and attributes. Section V
exhibits comprehensive experimental evaluation on various benchmarks, i.e.,
UAV123@10fps [22], DTB70 [23], and UAVDark135, where the superiority of
ADTrack is apparent. Finally, Section VI gives an integrated conclusion of the
full article.

##  II Related Work

###  II-A Low-Light Image Enhancers

Existing SOTA enhancers can be generally divided into 2 categories, i.e.,
learning-based and model-based. learning-based methods aim at training an end-
to-end network specialized for domain transformation with paired images [24,
25, 26]. To name a few, C. Chen et al. [25] carefully designed an encoder-
decoder structured network and trained it with paired short-exposure low-light
images and corresponding long-exposure images, which can generate high-quality
enhanced images. W. Ren et al. [26] proposed a deep hybrid network made up of
distinct content stream and edge stream, which can handle the degraded images
captured at night. Such methods are usually carried out on GPU due to their
huge amount of calculation brought by convolution layers, which cannot realize
real-time processing on a single CPU for tracking onboard UAV.

Model-based methods [27, 28, 21, 29] are in view of the famous retinex theory
[30], which need no off-line training. For instance, M. Li et al. [27]
creatively proposed to estimate a noise map based on tradition retinex model,
which is able to obtain de-noised enhanced images. Z. Rahman et al. [29]
replaced the original logarithm function in multi-scale retinex model with a
sigmoid function which can suppress noise speckles in extreme low light areas.

Specially, the enhancer [21] proves to be both efficient and effective. This
work introduces it into the UAV tracking structure and constructs an
illumination adaptive anti-dark tracker. Different from roughly applying the
enhancer to preprocess the frames, ADTrack achieves light condition awareness
and dual regression with the in-depth integration of image enhancement and
visual tracking.

###  II-B CF-Based Tracking Methods

The key idea of CF-based tracking methods is to train a discriminative
correlation filter using current image samples and utilize the trained filter
to search for the object in the coming frame [31, 32, 10, 33, 11, 9, 8, 34].
J. F. Henriques et al. introduced circular sample matrix, ridge regression and
kernel correlation in the KCF tracker [31], considered as the foundation of
all CF-based trackers. M. Danelljan et al. proposed scale filter in the DSST
tracker [11], settling scale estimation efficiently. H. K. Galoogahi et al.
put forward cropping matrix in the training regression equation [10], not only
immensely expanding the real negative samples but also alleviating boundary
effect.

Generally speaking, the key enablers of the superiority of CF type methods
involve: $1$) its simplicity of calculation by discrete Fourier transformation
as well as a myriad of implicitly circular shift samples generated in this
duration, $2$) its online learning schemes which make the filter maintain
satisfying robustness in various scenarios. UAV tracking, owing to its limited
computation resource and wide application, is right the field where CF-based
trackers can shine brilliantly [35, 17, 36, 9, 8]. To be specific, Z. Huang et
al. exploited response map aberrance and proposed the ARCF tracker [9], which
ensures high robustness under volatile tracking distractors. The AutoTrack
tracker [8] proposed by Y. Li et al. aimed at the onerous mode adjustment
procedure and adapted to various given condition automatically.

Though the trackers mentioned above can strike a balance between accuracy and
speed in common bright environment, they lose robustness and accuracy in the
dark, when the light condition becomes abominable. To realize all-day real-
time UAV tracking, this work proposed ADTrack, which can adapt to the given
light condition and maintain predominant robustness even in the terrible
darkness.

###  II-C Target-Aware CF-Based Tracking

Target-aware mask, which is a foreground matrix, concentrating the filter’s
attention on pixels more possible included within the object outline. Such
strategy can raise the importance of features indeed represent the object
characteristics. M. Danelljan et al. proposed a spatial punishment term in the
SRDCF tracker [32], which can be considered as a foreground mask, making the
filter learn center region more to alleviate boundary effect. A. Lukezic et
al. improved the fixed spatial regularization term in the SRDCF tracker [32]
into alterable reliability mask in the CSR-DCF tracker [37], which is based on
Bayes principle. Recently, saliency-aware methods are also introduced [35,
38]. Specifically, C. Fu et al. [35] creatively utilized an efficient saliency
detection method to generate effective mask, which raised the robustness
tracker onboard UAV tremendously.

Figure 3: Pipeline of ADTrack. The proposed tracker contains four stages,
i.e., illumination adaptation, pretreatment, training, and detection stages,
which are marked out by boxes in different colors. In well-illuminated daytime
and dim nighttime, ADTrack is able to adjust its tracking modules
automatically, according to light condition judgment in illumination
adaptation stage. In training and detection stage, ADTrack adopts dual filter
training and dual response fusion, respectively, target-focused level and
context level, as is shown in different routes.

Despite that the methods mentioned can improve tracking performance, they have
two obvious drawbacks. Firstly, it is hard to obtain valid alterable masks
like [37], [38], and [35] in the dark, since nighttime images lack sufficient
information. Secondly, the aforementioned trackers directly embed the masks
into the CF in training process as a regularization term, assigning higher
weights to the predicted target region in the filter. When an invalid mask
generates, wrong pixels that odd with actual target region of the CF will
possess higher importance, easily leading to tracking failure.

Totally different from the above, ADTrack exploits image illumination
information to obtain effective masks in arbitrary light condition. By virtue
of the target-aware mask, ADTrack proposes a dual regression, where target-
focused and context filters are trained with mutual constriction. In this way,
both background and target information are learned and utilized, greatly
improving tracking robustness.

##  III Illumination Adaptive and Anti-Dark Object Tracking

The pipeline of the proposed ADTrack can be divided into four stages, i.e.,
illumination adaptive stage, pretreatment stage, training stage, and detection
stage. As is clearly exhibited in Fig. 3, for a given dark tracking scene,
ADTrack firstly implements an illumination adaptation decider in the first
frame captured by UAV to judge whether it is at daytime or nighttime. The
determined outcome can enable mode switching automatically. In the subsequent
frame $f$, ADTrack carries out pretreament stage with the illumination
judgment result, where appropriate samples (from original image or enhanced
image) and target-aware mask are obtained simultaneously. Then in training
stage, dual filters are jointly trained by focusing on both context and target
appearance. As next frame $f+1$ comes, the trained filters generate dual
response maps which are fused to obtain the final response for target
localization as detection stage.

###  III-A Illumination Adaptation

To realize all-day adaptation, we consider an effective illumination
expression algorithm, which transforms complex image illuminance information
into a simple constant. For a given RGB image
$\mathcal{I}\in\mathbb{R}^{w\times h\times 3}$, the pixel-level world
illumination value $\mathcal{L}^{\mathrm{W}}(\mathcal{I})$ is firstly computed
as:

$\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})=\sum_{\mathrm{m}}\alpha_{\mathrm{m}}\Psi_{\mathrm{m}}(\mathcal{I}(x,y)),~{}\mathrm{m}\in\\{\mathrm{R,G,B}\\}~{},$
(1)

where $\Psi_{\mathrm{m}}(\mathcal{I}(x,y))$ denotes the intensity value of
image $\mathcal{I}$ at coordinate $(x,y)$ in color channel $\mathrm{m}$, e.g.,
$\Psi_{\mathrm{G}}(\mathcal{I}(x,y))$ denotes the value in green channel. The
channel weight parameters
$\alpha_{\mathrm{R}},\alpha_{\mathrm{G}},\alpha_{\mathrm{B}}$ meet
$\alpha_{\mathrm{R}}+\alpha_{\mathrm{G}}+\alpha_{\mathrm{B}}=1$. Then, the
log-average luminance $\tilde{\mathcal{L}}^{W}(\mathcal{I})$ is given as in
[20]:

$\tilde{\mathcal{L}}^{\mathrm{W}}(\mathcal{I})={\mathrm{exp}}\Big{(}\frac{1}{wh}\sum_{x,y}\mathrm{log}(\delta+\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I}))\Big{)}~{},$
(2)

where $\delta$ is a small value, to avoid zero value.

Remark 1: Our experiment has proved that the log-average luminance
$\tilde{\mathcal{L}}^{W}(\mathcal{I})$ can express the light condition of
image $\mathcal{I}$ effectively.

Different light condition, e.g., in the dark or in daytime, the log-average
luminance varies largely. Thus, ADTrack introduced a threshold $\tau$ for
illumination judgment as:

$S(\mathcal{I})=\left\\{\begin{array}[]{rcl}1&&{\tilde{\mathcal{L}}^{W}(\mathcal{I})<\tau}\\\
0&&{\tilde{\mathcal{L}}^{W}(\mathcal{I})\geq\tau}\end{array}\right.~{},$ (3)

where $S(\mathcal{L})$ can be seen as the night identifier, $S(\mathcal{I})=1$
indicates that image $\mathcal{I}$ is a night image.

Remark 2: To test the validity of the above night decider and determine a
proper $\tau$, this work selected first frames in benchmark UAV123@10fps [22]
as daytime test samples and newly constructed benchmark UAVDark135 as
nighttime test samples. The deciding success rate of different thresholds
$\tau$ are shown in TABLE I, where the decider can achieve a surprising result
of over 99%.

Remark 3: During UAV tracking, ADTrack implements illumination adaptation
decider in the first frame of a given sequence, then adjusts its mode and
following pretreatment stage automatically according to the judgment outcome
$S(\mathcal{I})$ in Eq. (3).

TABLE I: Success rates of proposed illuminance decider with different thresholds $\tau$. Clearly, the results can surprisingly achieve over 99%, enabling effective night judgment. Thresholds $\tau$ | 0.12 | 0.13 | 0.14 | 0.15 | 0.16 | 0.17 | 0.18 | 0.19
---|---|---|---|---|---|---|---|---
Success rate | 0.979 | 0.983 | 0.987 | 0.991 | 0.987 | 0.987 | 0.991 | 0.983

###  III-B Pretreatment

The pretreatment stage can achieve two purposes. Firstly, for determined night
sequences, ADTrack adopts an efficient and effective image enhancer [21] to
obtain enhanced images for the subsequent training and detection stages.
Secondly, the target-aware mask is acquired by virtue of image illuminance
information, so that dual filters learning in the training process and dual
response maps generation in the detection process can be realized.

Remark 4: To our excitement, the two purposes can be both based on world
illumination value $\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})$ in Eq. (1) and
log-average luminance $\tilde{\mathcal{L}}^{W}(\mathcal{I})$ in Eq. (2).

To realize image enhancing, the global adaptation factor
$\mathcal{L}_{\mathrm{g}}(x,y,\mathcal{I})$, which is based on the original
world illumination map, is firstly calculated as in [21]:

$\mathcal{L}_{\mathrm{g}}(x,y,\mathcal{I})=\frac{\mathrm{log}(\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})/\tilde{\mathcal{L}}^{\mathrm{W}}(\mathcal{I})+1)}{\mathrm{log}(\mathcal{L}^{\mathrm{W}}_{\mathrm{max}}(\mathcal{I})/\tilde{\mathcal{L}}^{\mathrm{W}}(\mathcal{I})+1)}~{},$
(4)

where
$\mathcal{L}^{\mathrm{W}}_{\mathrm{max}}(\mathcal{I})=\mathrm{\mathrm{max}}(\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I}))$.
The calculated factor can be referred to change the pixel value in three
intensity channels of each pixel to realize image enhancement as:

$\Psi_{\mathrm{m}}(\mathcal{I}_{\mathrm{e}}(x,y))=\Psi_{\mathrm{m}}(\mathcal{I}(x,y))\cdot\frac{\mathcal{L}_{\mathrm{g}}(x,y,\mathcal{I})}{\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})}~{},$
(5)

where $\mathcal{I}_{\mathrm{e}}$ denotes the enhanced image based on original
$\mathcal{I}$. Since $\mathcal{L}_{\mathrm{g}}(x,y,\mathcal{I})$ varies in
different regions that process different illumination, Eq. (5) can readjust
the brightness of the whole image while keeping the proportion of the three
color channels a constant, i.e., the image color unchanged. Remark 5: The
aforementioned strategy is merely the fast first stage of [21], Eq. (5) shows
its efficacy for image enhancing.

For target-aware mask generation, ADTrack considers the illuminance change
$\bm{\Theta}_{\mathcal{L}}(\mathcal{I})$ after enhancement, which can be
written as:

$\begin{split}\bm{\Theta}_{\mathcal{L}}(\mathcal{I})&=\mathcal{L}^{\mathrm{W}}(\mathcal{I})-\mathcal{L}^{\mathrm{W}}(\mathcal{I}_{\mathrm{e}})\\\
&=\frac{\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})-\mathrm{log}\Big{(}\frac{\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})}{\tilde{\mathcal{L}^{\mathrm{W}}}(\mathcal{I})+1}\Big{)}}{\mathrm{log}(\mathcal{L}^{\mathrm{W}}_{\mathrm{max}}(\mathcal{I})/\tilde{\mathcal{L}}^{\mathrm{w}}(\mathcal{I})+1)}~{}.\end{split}$
(6)

Remark 6: The illumination change
$\bm{\Theta}_{\mathcal{L}}(\mathcal{I})(x,y)$ of pixel $(x,y)$ in a given
image depends only on the first 2 terms in the numerator of Eq. (6), i.e.,
$\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})-\rm{log}(\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I}))$.
Since $\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})\in[0,1]$, the value of
$\bm{\Theta}_{\mathcal{L}}(\mathcal{I})$ apparently varies according to the
original illumination $\mathcal{L}^{\mathrm{W}}(x,y,\mathcal{I})$. Assume that
different objects’ illumination are different under similar light condition in
an image due to their various reflectivity. By virtue of Eq. (6), the class of
pixels can be indicated as the target or the context.

To be specific, consider that the target is located at the center part, the
average value $\mu$ and standard deviation $\sigma$ of the center region of
$\mathbf{\Theta}_{\mathcal{L}}$ are computed. Following a three-sigma
criterion in statistics, which reflects the probability distribution
characteristics of samples, pixels in the range $\mu\pm 3\sigma$ are
considered targets while others are the context. Then, a binary mask
$\mathbf{m}_{r}$ is generated as:

$\mathbf{m}_{r}(x,y)=\left\\{\begin{array}[]{rcl}1&&{\mu-3\sigma\leq\mathbf{\Theta}_{\mathcal{L}}(x,y)\leq\mu+3\sigma}\\\
0&&{\mathrm{else}}\end{array}\right.~{}.$ (7)

Ultimately, the expected mask is obtained by
$\mathbf{m}=\mathbf{m}_{r}\odot\mathbf{P}$, where $\odot$ denotes element-wise
product. $\mathbf{P}\in\mathbb{R}^{w\times h}$ is the cropping matrix, which
extracts the value of the target-size area in the middle of the raw mask
$\mathbf{m}_{r}$, and set the value of the remaining area to 0 to shield the
interference of similar brightness objects in the background. Fig. 4 displays
some representative examples of mask generation in all-day conditions.

Figure 4: Visualization of mask generation in both nighttime and daytime. From
top to bottom, the images denote original patch, illumination map, and
generated mask. The sequences person10_1, pedestrian5_2 are from newly
constructed UAVDark135, and boat2, person20 are from UAV123@10fps [22]. It’s
clear that in both conditions, the proposed method can obtain valid mask with
vivid object contour.

Thus, according to the outcome in Eq. (3), for $S(\mathcal{I})=1$, ADTrack
uses Eq. (5) to obtain enhanced image $\mathcal{I}_{e}$, and for
$S(\mathcal{I})=0$, original image $\mathcal{I}$ is utilized. Both daytime
sequences and nighttime sequences can adopt Eq. (6) for mask generation.

###  III-C Filter Training

####  III-C1 Review of BACF

Due to both its robustness and efficiency, this work adopts background-aware
correlation filter (BACF) [10] as baseline. The BACF tracker achieves its
satisfying performance mainly by virtue of the introduction of the cropping
matrix $\mathbf{P}$, which expands the training samples hugely without
inletting much boundary effect. The training regression equation of the BACF
tracker can be expressed as:

$\mathcal{E}(\mathbf{w})=\frac{1}{2}\sum_{j=1}^{T}\left\|\sum_{c=1}^{D}\mathbf{w}^{c\top}\mathbf{P}\mathbf{C}^{j}\mathbf{x}^{c}-\mathbf{y}(j)\right\|_{2}^{2}+\frac{\lambda}{2}\sum_{c=1}^{D}\left\|\mathbf{w}^{c}\right\|_{2}^{2}~{},$
(8)

where $\mathbf{w}^{c}\in\mathbb{R}^{N}(c=1,2,\cdots,D)$ is the filter in the
$c$-th channel obtained in current frame and
$\mathbf{w}=[\mathbf{w}^{1},\mathbf{w}^{2},\cdots,\mathbf{w}^{D}]$ denotes the
whole filter. $\mathbf{x}^{c}\in\mathbb{R}^{T}$ is the $c$-th channel of
extracted feature map and $\mathbf{y}(j)$ denotes the $j$-th element in the
expected Gaussian-shape regression label $\mathbf{y}\in\mathbb{R}^{T}$.
Cropping matrix $\mathbf{P}\in\mathbb{R}^{N\times T}$ aims at cropping the
center region of samples $\mathbf{x}^{c}$ for training and cyclic shift matrix
$\mathbf{C}^{j}\in\mathbb{R}^{T\times T}$ is the same in [31], which is
employed to obtain cyclic samples. $\lambda$ is the regularization term
parameter.

Remark 7: Since in Eq. (8), $T$ and $N$ meet $T>>N$, the filter $\mathbf{w}$
learns far more samples, the negative samples in particular, than in other CF-
based trackers. Such strategy makes the filter aware of the background
information, resulting in its better discriminative ability.

####  III-C2 Proposed ADTrack

Apart from BACF [10], which trains single filter $\mathbf{w}$ with both
negative and positive target-size samples, ADTrack trains dual filters
$\mathbf{w}_{g}$ and $\mathbf{w}_{o}$, which learns context and target
information separately. Besides, a constraint term is added into the overall
objective to promise more robust tracking on-the-fly. The proposed regression
objective can be written as:

$\begin{split}\mathcal{E}(\mathbf{w}_{g},\mathbf{w}_{o})=&\sum_{k}(\frac{1}{2}\left\|\sum_{c=1}^{D}\mathbf{P}^{\top}\mathbf{w}_{k}^{c}\star\mathbf{x}_{k}^{c}-\mathbf{y}\right\|_{2}^{2}\\!\\!+\frac{\lambda}{2}\sum_{c=1}^{D}\left\|\mathbf{w}_{k}^{c}\right\|_{2}^{2})\\\
&+\frac{\mu}{2}\sum_{c=1}^{D}\left\|\mathbf{w}_{g}^{c}-\mathbf{w}_{o}^{c}\right\|_{2}^{2}~{},k\in\\{g,o\\}~{},\end{split}$
(9)

where $\star$ denotes circular correlation operator, which implicitly executes
sample augmentation by circular shift. Differently, $\mathbf{x}_{g}$ denotes
the context feature map, while $\mathbf{x}_{o}$ indicates the target region
feature map, which is generated using the mask $\mathbf{m}$, i.e.,
$\mathbf{x}_{o}=\mathbf{m}\odot\mathbf{x}_{g}$. The second and fourth term in
Eq. (9) serve as the regularization term to prevent overfitting of the
filters. The last term can be considered as the constraint term, where
$\mathbf{w}_{g}$ and $\mathbf{w}_{o}$ bind each other during training. In this
case, the discriminative ability of both filters will be more robust. $\mu$ is
a parameter used to control the impact of the constraint term.

Remark 8: In order to maintain historic appearance information of object, this
work follows a conventional fashion in [10] for adaptive feature updates using
linear interpolation strategy with a fixed learning rate $\eta$ as:

$\begin{split}\mathbf{x}^{f}_{k,\mathrm{model}}=\mathbf{x}^{f-1}_{k,\mathrm{model}}+\eta\mathbf{x}^{f}_{k}~{},k\in\\{g,o\\}~{},\end{split}$
(10)

where $\mathbf{x}^{f}_{k,\mathrm{model}}$ denotes the training sample in the
$f$-th frame, which is utilized to train dual filters in Eq. (9).

####  III-C3 Optimization

Assume that $\mathbf{w}_{o}$ is given, ADTrack firstly finds the optimal
solution of $\mathbf{w}_{g}$. Defining an auxiliary variable $\mathbf{v}$,
i.e.,
$\mathbf{v}=\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}_{g}\in\mathbb{R}^{TD}$,
where $\otimes$ denotes Kronecker product, $\mathbf{I}_{N}$ an $N$-order
identical matrix. Here,
$\mathbf{w}_{g}=[\mathbf{w}^{1\top}_{g},\mathbf{w}^{2\top}_{g},\cdots,\mathbf{w}^{D\top}_{g}]^{\top}\in\mathbb{R}^{ND}$.
Then, the augmented Lagrangian form of Eq. (9) is formulated as:

$\begin{split}\mathcal{E}(\mathbf{w}_{g},\mathbf{v},\bm{\theta})&=\frac{1}{2}\left\|\mathbf{v}\star\mathbf{x}-\mathbf{y}\right\|^{2}_{2}+\frac{\lambda}{2}\left\|\mathbf{w}_{g}\right\|_{2}^{2}\\\
&+\frac{\mu}{2}\left\|\mathbf{w}_{g}-\mathbf{w}_{o}\right\|_{2}^{2}+(\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}_{g}-\mathbf{v})^{\top}\bm{\theta}\\\
&+\frac{\gamma}{2}\left\|\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}_{g}-\mathbf{v}\right\|^{2}_{2}~{},\end{split}$
(11)

where
$\bm{\theta}=[\bm{\theta}^{1\top},\bm{\theta}^{2\top},\cdots,\bm{\theta}^{D\top}]^{\top}\in\mathbb{R}^{TD}$
is the Lagrangian vector and $\gamma$ denotes a penalty factor. Adopting ADMM
[39], Eq. (11) can be dissected and solved by iteratively solving the
following three subproblems:

$\left\\{\begin{aligned}
\mathbf{w}^{e+1}_{g}&=\rm{arg}\min_{\mathbf{w}}\Big{\\{}\frac{\lambda}{2}\left\|\mathbf{w}^{e}_{g}\right\|_{2}^{2}+\frac{\mu}{2}\left\|\mathbf{w}^{e}_{g}-\mathbf{w}_{o}\right\|_{2}^{2}\\\
&+(\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}^{e}_{g}-\mathbf{v})^{\top}\bm{\theta}+\frac{\gamma}{2}\left\|\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}^{e}_{g}-\mathbf{v}\right\|^{2}_{2}\Big{\\}}\\\
\mathbf{v}^{e+1}&=\rm{arg}\min_{\mathbf{v}}\Big{\\{}\frac{1}{2}\left\|\mathbf{v}^{e}\star\mathbf{x}-\mathbf{y}\right\|^{2}_{2}+\\\
&(\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}_{g}-\mathbf{v}^{e})^{\top}\bm{\theta}+\frac{\gamma}{2}\left\|\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{w}_{g}-\mathbf{v}^{e}\right\|^{2}_{2}\Big{\\}}\\\
\bm{\theta}^{e+1}&=\bm{\theta}^{e}+\gamma(\mathbf{v}^{e+1}-(\mathbf{FP}^{\top}\otimes\mathbf{I}_{D})\mathbf{w}^{e+1}_{g})~{},\\\
\end{aligned}\right.$ (12)

where the superscript $\cdot^{e}$ indicates $e$-th iteration. Following
superscript ′ represents the optimization objectives.

Subproblem $\mathbf{w}^{\prime}_{g}$: By setting the partial derivative of the
first subproblem in Eq. (12) with respect to $\mathbf{w}_{o}$ as zero, we can
find the closed-form solution of $\mathbf{w}^{\prime}_{g}$, which is expressed
as:

$\mathbf{w}^{\prime}_{g}=\frac{\mu\mathbf{w}_{o}+T\bm{\theta}+\gamma
T\mathbf{v}}{\lambda+\mu+\gamma T}~{}.$ (13)

Subproblem $\mathbf{v}^{\prime}$: To effectively obtain the closed-form of
$\mathbf{v}$, this work firstly turn the second subproblem in Eq. (12) into
Fourier domain using discrete Fourier transform (DFT) as:

$\begin{split}\mathbf{v}^{\prime}=\rm{arg}\min_{\hat{\mathbf{v}}}&\Big{\\{}\frac{1}{2T}\left\|\hat{\mathbf{v}}^{*}\odot\hat{\mathbf{x}}-\hat{\mathbf{y}}\right\|^{2}_{2}+\hat{\bm{\theta}}^{\top}(\sqrt{T}\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{F}_{N}\mathbf{w}_{g}\\\
&-\hat{\mathbf{v}})+\frac{\gamma}{2T}\left\|\sqrt{T}\mathbf{I}_{N}\otimes\mathbf{P}^{\top}\mathbf{F}_{N}\mathbf{w}_{g}-\hat{\mathbf{v}}\right\|^{2}_{2}\Big{\\}}~{},\\\
\end{split}$ (14)

where $\hat{\cdot}$ denotes the Fourier form of a variable, i.e.,
$\hat{\mathbf{x}}=\sqrt{T}\mathbf{F}_{T}\mathbf{x}$.
$\mathbf{F}_{T}\in\mathbb{C}^{T\times T}$ is the Fourier matrix. Superscript
$\cdot^{*}$ indicates the complex conjugate.

Remark 9: Since circular correlation in time domain is turned into element-
wise product in Fourier domain, separating sample in Eq. (14) across pixels,
e.g.,
$\mathbf{x}(t)=[\mathbf{x}^{1}(t),\mathbf{x}^{2}(t),\cdots,\mathbf{x}^{D}(t)]\in\mathbb{R}^{T\times
D},(t=1,2,\cdots,T)$, each $\hat{\mathbf{v}}^{\prime}(t)$ can be solved as:

$\begin{split}\hat{\mathbf{v}}^{\prime}(t)=&\Big{(}\hat{\mathbf{x}}(t)\hat{\mathbf{x}}(t)^{\top}+T\gamma\mathbf{I}_{D}\Big{)}^{-1}\\\
&\times\Big{(}\hat{\mathbf{y}}(t)\hat{\mathbf{x}}(t)-T\hat{\bm{\theta}}(t)+T\gamma\hat{\mathbf{w}}_{g}(t)\Big{)}~{}.\end{split}$
(15)

Sherman-Morrison formula [40] is applied to avoid the time-consuming matrix
inversion operation and Eq. (15) is turned into:

$\begin{split}\hat{\mathbf{v}}^{\prime}(t)=\frac{1}{\gamma
T}\Big{(}\hat{\mathbf{y}}(t)\hat{\mathbf{x}}(t)-T\hat{\bm{\theta}}(t)+\gamma
T\hat{\mathbf{w}}_{g}(t)\Big{)}-\\\ \frac{\hat{\mathbf{x}}(t)}{\gamma
Tb}\Big{(}\hat{\mathbf{y}}(t)\hat{\mathbf{s}}_{\mathbf{x}}(t)-T\hat{\mathbf{s}}_{\bm{\theta}}(t)+\gamma
T\hat{\mathbf{s}}_{\bm{w}_{g}}(t)\Big{)}~{},\end{split}$ (16)

where
$\hat{\mathbf{s}}_{\mathbf{x}}(t)=\hat{\mathbf{x}}(t)^{\top}\hat{\mathbf{x}},\hat{\mathbf{s}}_{\bm{\theta}}=\hat{\mathbf{x}}(t)^{\top}\hat{\mathbf{\theta}},\hat{\mathbf{s}}_{\bm{w}_{g}}=\hat{\mathbf{x}}(t)^{\top}(t)\hat{\mathbf{w}}_{g}$,
and $b=\hat{\mathbf{s}}_{\mathbf{x}}(t)+T\gamma$ are scalar.

The positions of $\mathbf{w}_{g}$ and $\mathbf{w}_{o}$ in Eq. (9) are
equivalent. When an solving iteration of $\mathbf{w}_{g}$ is completed, then
the same ADMM iteration operation is performed to obtain the optimized
solution of $\mathbf{w}_{o}$.

###  III-D Target Detection

Given the expected filter $\mathbf{w}^{f}_{g}$ and $\mathbf{w}^{f}_{o}$ in the
$f$-th frame, the response map $\mathbf{R}$ regarding the detection samples
$\mathbf{z}^{f+1}$ in the $(f+1)$-th frame can be obtained by:

$\begin{split}\mathbf{R}=\mathcal{F}^{-1}\sum_{c=1}^{D}\big{(}\hat{\mathbf{w}}^{f,c*}_{g}\odot\hat{\mathbf{z}}^{f+1,c}_{g}+\psi\hat{\mathbf{w}}^{f,c*}_{o}\odot\hat{\mathbf{z}}^{f+1,c}_{o}\big{)}~{},\end{split}$
(17)

where $\mathcal{F}^{-1}$ means inverse discrete Fourier transform.
$\mathbf{z}_{g}^{f+1,c}$ denotes the $c$-th channel of resized search region
samples extracted in the $(f+1)$-th frame, and $\mathbf{z}_{o}^{f+1,c}$ is the
$c$-th channel of the masked samples similar to $\mathbf{x}_{o}$. $\psi$ is a
weight parameter that controls the impact response map generated by context
filter and object filter. Finally, the object location in the $(f+1)$-th frame
can be estimated at the peak of response map $\mathbf{R}$.

The holonomic pipeline pseudo code of ADTrack is summarized in Algorithm 1.

1

Input: A video sequence of $F$ frames.

Position ($\mathbf{p}^{1}$) and size ($\mathbf{s}^{1}$) of the tracked object

in the first frame $\mathcal{I}^{1}$.

Output: Estimated position ($\mathbf{p}^{f}$) and size ($\mathbf{s}^{f}$) of
the object in all upcoming frames.

2 Construct the Gaussian label function $\mathbf{y}$.

3 for  _frame number $f=1$ to end_ do

4 if  _$f=1$_ then

5 Calclate log-average illuminance
$\tilde{\mathcal{L}}^{\mathrm{W}}(\mathcal{I}^{1})$ and dark identifier
$S(\mathcal{I}^{1})$ and adjust the mode of the tracker (Sec. III-A).

6 Crop the training patch from $\mathcal{I}^{1}$ with $\mathbf{p}^{1}$ and
$\mathbf{sc}\times\mathbf{s}^{1}$, where $\mathbf{sc}$ is a predefined scale
factor.

7 if  _$S(\mathcal{I}^{1})==1$_ then

8 Do image enhancing to obtain enhanced patch (Sec. III-B).

9 end if

10 Obtain target-aware mask $\mathbf{m}$ (Sec. III-B).

11 Extract context features $\mathbf{x}^{1}_{g}$ and target features
$\mathbf{x}^{1}_{o}$ of the obtained patch.

12 Update the appearance model
$\mathbf{x}^{1}_{k,\mathrm{model}}=\mathbf{x}^{1}_{k}~{},k\in\\{g,o\\}$ for
filter training.

13 Learn context and target filter $\mathbf{w}_{k}~{},k\in\\{g,o\\}$ (Sec.
III-C).

14

15 else

16 Crop the search patch from $\mathcal{I}^{f}$ with $\mathbf{p}^{f-1}$ and
$\mathbf{sc}\times\mathbf{s}^{f-1}$.

17 if  _$S(\mathcal{I}^{1})==1$_ then

18 Do image enhancing to obtain enhanced patch (Sec. III-B).

19 end if

20 Obtain target-aware mask $\mathbf{m}$ (Sec. III-B).

21 Extract context and target search features
$\mathbf{z}^{f}_{k}~{},k\in\\{g,o\\}$ of the obtained patch.

22 Generate the fused response map $\mathbf{R}$ (Sec. III-D).

23 Estimate $\mathbf{p}^{f}$ and $\mathbf{s}^{f}$.

24 Crop the training patch from $\mathcal{I}^{f}$ with $\mathbf{p}^{f}$ and
$\mathbf{sc}\times\mathbf{s}^{f}$.

25 if  _$S(\mathcal{I}^{1})==1$_ then

26 Do image enhancing to obtain enhanced patch (Sec. III-B).

27 end if

28 Obtain target-aware mask $\mathbf{m}$ (Sec. III-B).

29 Extract context features $\mathbf{x}^{f}_{g}$ and target features
$\mathbf{x}^{f}_{o}$ of the obtained patch.

30 Update the appearance model using Eq. (10) for dual filter training.

31 Learn context and target filter $\mathbf{w}_{k}~{},k\in\\{g,o\\}$ (Sec.
III-C).

32 end if

33

34 end for

Algorithm 1 ADTrack tracker

##  IV UAVDark135 Tracking Benchmark

TABLE II: Numbers of sequences, minimum, maximum, mean frames in each sequence, and total frames in 6 benchmarks, i.e., newly constructed UAVDark135, UAV123, UAV123@10fps [22], DTB70 [23], UAVDT [41], and VisDrone2019-SOT [42]. Red, green, and blue denotes the first, second and third place respectively. Benchmark | UAVDark135 | UAV123 | UAV123@10fps | DTB70 | UAVDT | VisDrone2019-SOT
---|---|---|---|---|---|---
Sequences | 135 | 123 | 123 | 70 | 50 | 132
Each sequence | 216 | 4571 | 929 | 109 | 3085 | 915 | 37 | 1029 | 306 | 68 | 699 | 225 | 82 | 2969 | 742 | 90 | 2970 | 833
Min | Max | Mean
Total frames | 125466 | 112578 | 37607 | 15777 | 37084 | 109909
TABLE III: Detailed explanation of the attributes in newly built UAVDark135, which are commonly confronted in UAV tracking. Attributes | Explanation
---|---
VC |  |  Viewpoint Change: In the sequence, different aspects, e.g.,
---
front, side, and top aspect, of the
tracking object are captured and involved.
FM |  |  Fast Motion: There exist two continuous frames, where the
---
center locations of the tracking object change
more than 20 pixels.
LR |  |  Low Resolution: There exist frames, where the
---
tracking object is small, whose total resolution is
fewer than 20 pixels.
OCC |  |  Occlusion: There exist frames, where the
---
tracking object is partially or
fully occluded by obstacles.
IV |  |  Illumination Variation: In the sequence, the
---
tracking object undergoes various light conditions.

###  IV-A Platform and Statistics

Standing as the first UAV dark tracking benchmark, the UAVDark135 contains
totally 135 sequences captured by a standard UAV222This work utilized Parrot
Bebop 2 drone as shooting platform. More detailed information can be found at
https://support.parrot.com/us/support/products/parrot-bebop-2. at night. The
benchmark includes various tracking scenes, e.g., crossings, t-junctions,
road, highway, and consists of different kinds of tracked objects like people,
boat, bus, car, truck, athletes, house, etc. To extent the covered scenes, the
benchmark also contains some sequences from YouTube, which were shot on the
sea. The total frames, mean frames, maximum frames, and minimum frames of the
benchmark are 125466, 929, 4571, and 216 respectively, making it suitable for
large-scale evaluation. TABLE LABEL:tab:benchmarks exhibits main statistics of
UAVDark135 against existing UAV tracking benchmarks, i.e., UAV123@10fps,
UAV123 [22], DTB70 [23], UAVDT [41], and VisDrone2019-SOT [42]
(VisDrone2019-SOT testset-challenge is not included). The videos are captured
at a frame-rate of 30 frames/s (FPS), with the resolution of 1920$\times$1080.

Remark 10: Despite the fact that there exist some sequences captured at night
in benchmark UAVDT [41] and VisDrone2019-SOT [42], it is far from an
exhaustive dark tracking evaluation. Besides, the night sequences are actually
well-illuminated in [41, 42], which can not represent more common dark
tracking scenes in UAVDark135, where the light conditions are much more hash.

###  IV-B Annotation

The frames in UAVDark135 are all manually annotated, where a sequence is
completely processed by the same annotator to ensure consistency. Since in
some dark scenes the object is nearly invisible, annotation process is much
more strenuous. After the first round, 5 professional annotators carefully
checked the results and made revision for several rounds to reduce errors as
much as possible in nearly 2 months.

Since the boundary contour of the object is not obvious in the dark, the
result boxes of the first annotation fluctuates in continuous image frames.
However, the actual motion process of the object should be smooth. In these
considerations, we record the original annotation every 5 frames for the
sequence with extremely severe vibration, and the results of the remaining
frames are obtained by linear interpolation, which is closer to the position
and scale variation of the real object.

Figure 5: Sequences distribution comparison of 6 UAV tracking benchmarks. The
abscissa are 5 attributes, and the ordinate is the numbers of the sequences.
The sequences in different benchmarks are marked by different colors, which
are explained in the legend. Note that not all benchmarks made contribution to
all 5 attributes.

(a)

(b)

(c)

Figure 6: Overall performance of SOTA handcrafted CF-based trackers on
UAV123@10fps [22], DTB70 [23], and newly built UAVDark135. The evaluation
metric in precision plot is the distance precision (DP) under center location
error (CLE) = 20 pixels, and the metric in success rate plot is the area under
curve (AUC). Clearly ADTrack maintains its robustness in all 3 benchmarks by
virtue of its dual regression.

###  IV-C Attributes

To better evaluate the trackers’ abilities under special challenges,
UAVDark135 also provides 5 commonly encountered challenge attributes in UAV
tracking, following our prior work [43], i.e., viewpoint change (VC), fast
motion (FM), low resolution (LR), occlusion (OCC), and illumination variation
(IV). TABLE III elaborately explains the criterion for each attribute.
Additionally, Fig. 5 displays sequences distribute comparison of 6 UAV
tracking benchmarks. Clearly, UAVDark135 distributes both evenly and
considerably in the five attributes.

##  V Experiment and Evaluation

TABLE IV: Average results by all 328 sequences of handcrafted trackers on benchmarks UAV123@10fps [22], DTB70 [23], and newly constructed UAVDark135. Red, green, and blue denotes the first, second and third place respectively. Here, the abilities of the trackers under all-day conditions are evaluated. Tracker | ADTrack | AutoTrack | ARCF-HC | ARCF-H | MCCT-H | STRCF | KCC | fDSST | DSST | BACF | CSR-DCF | ECO-HC | Staple_CA | Staple | KCF | SRDCF | SAMF
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Venue | Ours | ’20CVPR | ’19ICCV | ’19ICCV | ’18CVPR | ’18CVPR | ’18AAAI | ’17TPAMI | ’17TPAMI | ’17ICCV | ’17CVPR | ’17CVPR | ’17CVPR | ’16CVPR | ’15TPAMI | ’15ICCV | ’14ECCV
DP | 0.659 | 0.651 | 0.638 | 0.591 | 0.514 | 0.611 | 0.459 | 0.465 | 0.420 | 0.581 | 0.576 | 0.601 | 0.495 | 0.510 | 0.391 | 0.566 | 0.434
AUC | 0.480 | 0.468 | 0.462 | 0.433 | 0.380 | 0.451 | 0.330 | 0.354 | 0.321 | 0.429 | 0.415 | 0.449 | 0.367 | 0.377 | 0.266 | 0.427 | 0.312
FPS | 31.621 | 45.485 | 22.585 | 32.320 | 44.858 | 20.438 | 29.393 | 122.976 | 58.113 | 33.911 | 9.274 | 53.571 | 46.829 | 81.216 | 374.912 | 8.583 | 7.518

This section displays exhaustive experimental evaluations, involving night
benchmark UAVDark135, daytime benchmark UAV123@10fps [22], and DTB70 [23]. In
subsection V-A, implementation details including experiment platform,
parameter settings, features, and metrics are introduced. Subsection V-B gives
a comprehensive comparison of the handcrafted CF-based trackers on the
benchmarks, where the superiority of proposed ADTrack for all-day UAV tracking
is demonstrated. Subsection V-C presents attribute-based evaluation of the
handcrafted CF-based trackers to test their abilities under UAV special
challenges. In subsection V-D, we also invite SOTA deep trackers that utilize
convolution neural network (CNN) for dark tracking comparison. Lastly, in
subsection V-E, we extended ablation study and parameter analysis to further
demonstrate the validity of different modules in proposed ADTrack.

###  V-A Implementation Details

####  V-A1 Platform

The experiments extended in this work were mainly performed on MATLAB R2019a.
The main hardware adopted consists of an Intel Core I7-8700K CPU and 32GB RAM.

####  V-A2 Parameters

To guarantee the fairness and objectivity of the evaluation, the tested
trackers from other works have maintained their official initial parameters.

The different parameters of two conditions in ADTrack are as follows: In
daytime mode, $\mu$ is set as 280. During detection, weight $\psi$ is set as
$0.02$. Translation filter takes learning rate $\eta_{t}=0.032$ for model
update and for scale filter, $\eta_{s}$ is set as 0.016. In nighttime mode,
$\mu$ is set as 200. During detection, weight $\psi$ is set as $0.01$.
Translation filter takes learning rate $\eta_{t}=0.024$ for model update and
for scale filter, $\eta_{s}$ is set as 0.023.

Remark 11: ADTrack can adapt to the given light condition in its first stage,
where the tracking mode mentioned is switched automatically without manually
adjusting.

####  V-A3 Features and Scale Estimation

ADTrack uses handcrafted features for appearance representations, i.e., gray-
scale, a fast version of histogram of oriented gradient (fHOG) [44], and color
names (CN) [45]. Note that gray-scale and CN features can be valid in ADTrack
thanks to low-light enhancement. The cell size for feature extraction is set
as $4\times 4$. ADTrack adopts the scale filter proposed by [11] to perform
accurate scale estimation.

####  V-A4 Metrics

In the experiment evaluation, we mainly use two metrics: distance precision
(DP) and area under curve (AUC). DP is based on the distance between the
center points of the predicted box and the target ground-truth, and AUC is
based on the intersection ratio of the predicted box and the target ground-
truth box.

###  V-B Overall Evaluation

Using merely handcrafted features, most handcrafted CF-based trackers can
achieve satisfying running speed, by virtue of their light calculation, while
ensuring their robustness under various tracking scenes onboard UAV. This work
employs proposed ADTrack and 16 SOTA handcrafted CF-based trackers, i.e.,
AutoTrack [8], KCF [31], SAMF [46], SRDCF [32], STRCF [33], BACF [10], DSST &
fDSST [11], ECO-HC [47], ARCF-HC & ARCF-H [9], KCC [48], MCCT-H [49], CSR-DCF
[37], Staple [34], and Staple_CA [50], for evaluation on tracking benchmarks,
UAV123@10fps [22], DTB70 [23], and UAVDark135 to demonstrate the robustness of
the proposed ADTrack in all-day UAV tracking comprehensively.

Figure 7: Visualization of some typical tracking scenes in both daytime and
nighttime. Sequences Car2, RcCar6, and uav3 are from the daytime benchmarks
DTB70 and UAV123@10fps. Sequences car13, pedestrian_l, and running_man are
from the new nighttime benchmark UAVDark135. Clearly, ADTrack favorably
maintains its robustness in all-day UAV tracking challenges. The typical
sequences were made into video, which can be found at
https://youtu.be/cJMUKF4J38A.

####  V-B1 Daytime Performance

In Fig. 6, DP and AUC comparison on benchmarks, respectively, UAV123@10fps
[22], DTB70 [23] is exhibited in the first 2 columns, where ADTrack ranks
first in both metrics. Specifically, in Fig. 6, ADTrack surpasses the second-
best AutoTrack tracker (0.671) [8] by 1.6% (0.682). In terms of AUC, ADTrack
surpasses its baseline BACF tracker (0.413) [10] by over 19% (0.482). Fig. 6
shows that ADTrack brings its baseline (0.581) up by 24% (0.722) in DP, and
exceeds the brilliant AutoTrack tracker (0.478) by nearly 4% (0.497) in AUC.
The outstanding results achieved by ADTrack in daytime indicates its strong
robustness in real-world UAV tracking scenes by virtue of its dual filter
learning.

####  V-B2 Nighttime Performance

Fig. 6 displays the excellent handcrafted CF-based trackers’ DPs under newly
constructed benchmark UAVDark135, where clearly ADTrack exceeds all other
trackers, surpassing the second best tracker (0.599) by over 1% (0.605).
Additionally, ADTrack enjoys its satisfying advantages in success rate as
well, exceeding the baseline tracker (0.460) by over 1.9% (0.469). In dark
scenes like UAVDark135, ADTrack maintains its robustness, providing a
favorable choice for UAV all-day tracking.

TABLE V: Average performance of the handcrafted trackers by UAV special attributes. Obviously, ADTrack maintains its superiority in most challenges. Tracker Metric | DP | AUC
---|---|---
VC | FM | LR | OCC | IV | VC | FM | LR | OCC | IV
ADTrack | 0.637 | 0.63 | 0.668 | 0.622 | 0.605 | 0.464 | 0.464 | 0.471 | 0.434 | 0.437
AutoTrack | 0.622 | 0.588 | 0.651 | 0.598 | 0.599 | 0.448 | 0.433 | 0.455 | 0.412 | 0.431
ARCF-HC | 0.61 | 0.61 | 0.649 | 0.595 | 0.597 | 0.438 | 0.448 | 0.458 | 0.417 | 0.433
ARCF-H | 0.565 | 0.551 | 0.606 | 0.537 | 0.56 | 0.413 | 0.4 | 0.427 | 0.373 | 0.411
MCCT-H | 0.504 | 0.471 | 0.504 | 0.503 | 0.476 | 0.366 | 0.361 | 0.367 | 0.353 | 0.353
STRCF | 0.584 | 0.568 | 0.611 | 0.584 | 0.59 | 0.424 | 0.43 | 0.442 | 0.406 | 0.437
KCC | 0.425 | 0.451 | 0.493 | 0.427 | 0.459 | 0.309 | 0.329 | 0.348 | 0.297 | 0.326
fDSST | 0.462 | 0.406 | 0.481 | 0.436 | 0.424 | 0.343 | 0.327 | 0.363 | 0.317 | 0.329
DSST | 0.425 | 0.342 | 0.413 | 0.385 | 0.391 | 0.316 | 0.275 | 0.303 | 0.274 | 0.298
BACF | 0.55 | 0.554 | 0.582 | 0.517 | 0.537 | 0.41 | 0.411 | 0.414 | 0.371 | 0.402
CSR-DCF | 0.57 | 0.536 | 0.576 | 0.561 | 0.54 | 0.399 | 0.383 | 0.405 | 0.387 | 0.381
ECO-HC | 0.584 | 0.524 | 0.572 | 0.599 | 0.56 | 0.434 | 0.409 | 0.426 | 0.423 | 0.421
Staple_CA | 0.476 | 0.465 | 0.534 | 0.484 | 0.486 | 0.353 | 0.353 | 0.387 | 0.346 | 0.36
Staple | 0.463 | 0.498 | 0.567 | 0.491 | 0.512 | 0.343 | 0.379 | 0.407 | 0.349 | 0.377
KCF | 0.376 | 0.31 | 0.38 | 0.363 | 0.353 | 0.251 | 0.227 | 0.262 | 0.242 | 0.24
SRDCF | 0.526 | 0.549 | 0.587 | 0.509 | 0.55 | 0.403 | 0.418 | 0.43 | 0.365 | 0.418
SAMF | 0.414 | 0.36 | 0.427 | 0.418 | 0.391 | 0.293 | 0.267 | 0.303 | 0.288 | 0.281

####  V-B3 All-Day Performance

To evaluate the abilities of the trackers in all-day tracking scenes, this
part takes the average results on 3 benchmarks by sequences, i.e. daytime
benchmark UAV123@10fps [22], DTB70 [23], and nighttime benchmark UAVDark135,
together 328 sequences. TABLE IV exhibits the average results of SOTA
handcrafted CF-based trackers. Obviously, the proposed ADTrack possesses great
advantages over all the other trackers in both DP and AUC. In specific,
ADTrack (0.659) improves the precision of its baseline (0.581) by more than
13%, surpassing second place (0.651) by over 1%. In terms of AUC, ADTrack
(0.480) is far ahead of the second place (0.468), up nearly 2.6%. Fig. 7
displays some representative tracking scenes in all-day condition, where
ADTrack exhibits competitive in robustness against other arts. In addition to
the satisfying tracking performance, ADTrack achieves an average speed of over
31 FPS on a single CPU, meeting real-time requirement of onboard UAV tracking.

Evidently, ADTrack achieves promising tracking performance in all-day
condition, through day and night, thus greatly expanding the tracking based
application onboard UAV around-the-clock.

###  V-C Attribute-Based Evaluation

To clearly evaluate the abilities of the tracker under UAV specific challenge,
this part displays their performance in the aforementioned 5 UAV tracking
attributes, i.e., VC, FM, LR, OCC, and IV. TABLE V gives the average results
on all 328 sequences on 3 benchmarks. For the authoritative daytime
benchmarks, this subsection follows our precious work [43] to rewrite their
official attributes. Clearly, ADTrack outperforms all other trackers in most
attributes under 2 evaluation metrics. Specially, in FM, ADTrack surpasses
second-best tracker ARCF-HC [9] by over 3% in both DP and AUC. The results
demonstrate the satisfying comprehensive tracking performance and favorable
robustness of ADTrack in common challenges.

###  V-D Against Deep Trackers

This subsection focuses on comparison between proposed ADTrack and deep
trackers which utilize off-line trained deep network for feature extraction or
template matching. This work invites totally 11 SOTA deep trackers, i.e.,
SiamRPN++ [18], DaSiamRPN [51], SiamFC++ [15], ASRCF [52], ECO [47], UDT+
[14], HCFT [53], CoKCF [54], CFWCR [55], DeepSTRCF [33], and MCCT [49], to
evaluate their performance in UAVDark135. From Fig. 8, ADTrack outperforms all
deep trackers in terms of DP and AUC under benchmark UAVDark135.

Using merely single CPU, ADTrack still achieves a real-time speed at over 30
FPS, while many deep trackers are far from real-time even on GPU,
demonstrating the excellence of ADTrack for real-time UAV tracking against the
deep trackers.

Remark 12: The results illustrate that the top-ranked deep trackers in recent
years, especially the trackers without online update, e.g., SiamRPN++ [18],
DaSiamRPN [51], SiamFC++ [15], fail to maintain their robustness in real-world
common dark scenes, since the off-the-shelf CNNs they utilize are trained by
daytime images, ending up in their huge inferiority compared with online-
learned ADTrack in the dark. Since there lacks sufficient dark images for
training, off-line trained deep trackers fall short onboard UAV tracking at
night.

Figure 8: Comparison of proposed ADTrack and the deep trackers on self-
constructed UAVDark135. Evidently, most deep trackers fall short in hash dark
scenes, while ADTrack maintains its robustness.

###  V-E Ablation Study and Parameter Analysis

####  V-E1 Component Validity Analysis

To demonstrate the effectiveness of the proposed components, ablation study is
conducted on all three benchmarks. The average AUC results by sequences on 3
benchmarks are displayed in Fig. 9, where from bottom to top, proposed
components, i.e., weight sum, dual filter constraint, and illumination
adaptation, were disabled one by one. ADTrack_aed denotes ADTrack without the
weight sum in detection phase. In ADTrack_ae, dual filter training constraint
is disabled on the basis of ADTrack_aed. ADTrack_a denotes ADTrack_ae without
the image enhancer. And ADTrack_b_day, ADTrack_b_dark respectively denotes
ADTrack with daytime parameters and nighttime parameters without any proposed
module. The first 3 bars in Fig. 9 has demonstrated the validity of
illumination adaptation module, and the last 3 bars illustrates how dual
filter constraint and weight sum boosts the trackers’ performance.

####  V-E2 Impacts of Key Parameters

The key parameters in ADTrack are the constraint parameter $\mu$ in the
training regression equation and weight parameter $\psi$ in detection stage.
We investigate the impact of two parameters on tracking results on nighttime
benchmark UAVDark135, i.e., DP and AUC, which is shown in Fig. 10. Note that
when $\mu$ varies, $\psi$ remains 0.01 unchanged, and when $\psi$ changes,
$\mu$ is settled as 200.

Figure 9: Ablation study of the modules in ADTrack. ADTrack, ADTrack_aed,
ADTrack_ae, ADTrack_a, ADTrack_b_day, and ADTrack_b_dark respectively denote
ADTrack with different component activated. Note that ADTrack_ae performs
slightly inferior than ADTrack_a since the enhancer needs to cooperate with
the dual filter learning and dual response fusion modules to work effectively.
Figure 10: Parameter analysis of ADTrack on newly built benchmark UAVDark135.
With other parameters remaining fixed, the tracking performance with different
$\mu$ (Top) and with different $\psi$ (Bottom) are displayed. The chosen
parameters along with their results are marked out by dotted lines.

##  VI Conclusion

This work puts forward a novel real-time tracker with illumination adaptation
and anti-dark function, i.e., ADTrack. ADTrack first implements illumination
adaptation to decide day-night condition and switches its tracking mode. Then,
pretreatment is carried out where proper training patch and target-aware mask
are generated based on an image enhancer. With the mask, ADTrack proposes
innovative dual filter regression model, in which the dual filters restrict
each other in training and compensate each other in detection. In addition,
the first large-scale dark tracking benchmark, UAVDark135, is also built in
this work for visual tracking community. We strongly believe that the proposed
tracker and dark tracking benchmark will make outstanding contribution to the
research of UAV tracking in all-day conditions.

## Acknowledgment

This work is supported by the National Natural Science Foundation of China
(No. 61806148) and Natural Science Foundation of Shanghai (No. 20ZR1460100).

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