been performed: manual and subject self-evaluation. The dataset has been
summarised in table 18. RGB videos and 16-bit depth images of the face and
upper body of the subject were recorded at a frame rate of 25 frames per
second.

Table 17: Task description for the BAREM dataset anomaly generation

Task 1 |  | The subject is provided with an email and username, and is asked to sign up on the platform.
---
Cause of frustration: The username provided to the subject is already in
existence, thus leading to an error message
during registration process. The subject must select another username in order
to accomplish the task.
Task 2 |  | Here, the subject has to fill up data about two children.
---
The objective is to give a sense of accomplishment to the subject and beef up
the frustration level in the succeeding task.
Task 3 |  | The subject has to book school transportation for the first child. A part of data must be obtained from a document that is
---
provided during the start of the scenario. It is an essential requirement to
upload a file available on the computer in dual
formats.
Cause of frustration: The first attempt while uploading the first file will
cause a format error. The subject will have to
perform the upload again with the second file.
Task 4 |  | The subject has to now book school transportation for the second child.
---
Cause of frustration: Essential data related to the itinerary is lacking,
making task completion out of the question. This
results in a strong impact on the subject’s level of frustration.

Table 18: Summary: BAREM dataset

# subjects | 18 (11 men, 7 women)
---|---
# videos | 72 (4 tasks per subject)
Setting | Interaction with a flawed e-service platform
Annotation | Manual (presence/absence of behaviour changes);
| Subject self-evaluation (level of frustration)
Modalities | RGB & depth videos (face & upper body);
| Audio (voice, breath and keyboard input force);
| Screen captures;
| Physiological signals (BVP, EEG, EDA);
| Navigation logs

##  7 Discussion on Audio-visual datasets

In this section, we present a discussion on the audio-visual datasets
described in the previous section. There are very few audio-visual datasets
for anomaly detection. These datasets focus on the specific type of anomaly.
VSD dataset, XD-Violence dataset, etc., consist of only fight events, EMOLY
dataset has an anomalous mood of a person as an anomaly, Human-human
interaction dataset has agitation behavior as an anomaly. Apart from having
one class of anomaly, the datasets are mainly recorded in a controlled
environment (except for fight anomaly, which is easy to collect, too) by some
human actors. The existing audio-visual dataset is good for application-
specific anomaly detection, but there is a lack of the same for generic scene
surveillance. Some researchers have tried collecting such datasets, but they
are not released publicly due to privacy or legal issues.

Table 19 lists the audio-visual datasets with their year of release, number of
citations, and its application. We can see that datasets for agitation
detection (Human-human Interaction) and violence detection (VSD and XD-
Violence) have been much in use compared to others due to their applicability
in real threat indication. The audio-visual datasets have been designed and
made available to the users recently. Different areas of applications have
been covered for these datasets. Some of these include anomalous expression
detection, violence detection, detection of stress in a particular situation,
etc.

Table 20 details dataset collection, viz., whether the dataset is untrimmed,
at how many locations the dataset is collected, scene in the dataset,
environment of the dataset, and the density of moving objects in the scene. It
can be observed from the table that none of the datasets is collected in
continuation and hence is not suitable for evaluation of adaptive anomaly
detection frameworks. The dataset collection scenarios are mostly limited to
indoor, except for VSD and XD-Violence, which consist of both indoor and
outdoor environments. The datasets for violence detection, which are mainly
collected from real CCTV footage, have high density; otherwise, the rest other
datasets which contain actors have low density.

Dataset comparison on the basis of anomaly information is presented in Table
21. All the datasets have frame-level annotations, and some acted anomalies
too. The type of anomalies is only one except for the datasets for violence
detection, which consist of 8 anomalies for VSD and 6 for XD-Violence
datasets. Some of the example anomalies include fights, stress, explosions,
abuse, car accident, shooting, frustration, etc.

Table 22 provides details about features and specifications of the audio-
visual clips. Mainly, information about the total number of videos and total
duration is made available by the authors. The datasets for violence detection
records the largest length of 217 hours compared to others; this is because
violence event is comparatively less rare to occur and hence easily available
in CCTV-footage. The total number of videos available for XD-Violence is
maximum, i.e., 4754, followed by EMOLY dataset consisting of 123 videos. Least
number of videos have been provided in the Human-human interaction dataset,
with the total duration of 32.24 minutes. Clips have been recorded at multiple
resolutions and at multiple frame rates. However, the sampling rate of audios
recorded in the databases have not been made available by the authors. Few
clips from VSD as well as XD-Violence datasets consist of camera motion, thus
making them challenging for further analysis.

Table 19: Applications of various audio-visual datasets Dataset | Year | # Ref. | Area of Application
---|---|---|---
Human-human Interaction | 2014 | 23 | Detection of stressful situations at a help-desk [197]
VSD | 2015 | 43 | violence detection [198, 199, 101]
EMOLY | 2018 | 1 | abnormal expression detection [194]
XD-Violence | 2020 | 15 | violence detection [200], anomaly detection [201]
BAREM | 2021 | 0 | Behaviour Analysis for Reverse Efficient Modeling [196]
Table 20: Comparison of audio-visual datasets based on data collection information Dataset | Continuity | Location | Scene | Indoor/Outdoor | Density
---|---|---|---|---|---
Human-human Interaction | no | 1 | help-desk | indoor | low
VSD | no | multiple | multiple | both | high/medium/low
EMOLY | no | 1 | lab | indoor | low
XD-Violence | no | multiple | multiple | both | high/medium/low
BAREM | no | 1 | e-service platform | indoor | low
Table 21: Comparison of audio-visual datasets based on anomaly information

Dataset | Ground truth |  | Nature of
---
Anomaly
# Actor |  | Types of
---
Anomaly
Example anomalies/event
Human-human Interaction | frame | acted | 9 | 1 | stress
VSD | frame | both | - | 8 | fights, fire, gunshots, cold weapons, car chases,
|  |  |  |  | gory, explosions, screams
EMOLY | frame | both | 41 | 1 | anomalous expression
XD-Violence | frame | both | - | 6 | abuse, car accident, explosion, fight, riot, shoot
BAREM | frame | both | 18 | 1 | frustration

Table 22: Specifications of audio-visual datasets

Dataset | Release | Total | Total No. of | Total No. of | Video | FPS (video), | Camera
---|---|---|---|---|---|---|---
| Year | Duration | Frames | Videos | resolution |  | motion
Human-human Interaction | 2014 | 32.24 min | - | 8 | - | - | none
VSD | 2015 | 35 hrs 18 min | - | 25 | multiple | multiple | in some
EMOLY | 2018 | - | - | 123 | - | - | none
XD-Violence | 2020 | 217 hrs | - | 4754 | multiple | multiple | in some
BAREM | 2021 |  $\approx$ 6 hrs |  $\approx$5,40,000 | 72 | - | 25 | none

##  8 DISCUSSION: TOWARDS THE FUTURE

During the last decade, there has been a drastic shift from datasets with less
number of anomaly samples and total duration to datasets with diverse range of
anomalies and gigantic volume. It may be observed that availability of
datasets with crime specific anomalies have facilitated development of
automated surveillance framework for crime detection. For videos, the category
spans as detection of explosion, abuse, panic-escape, assault, accident,
violence, fight, etc. In case of audios, they span as detection of gunshot,
shout, etc. Further, for audio-visual dataset, the categories span as riots,
detection of fight and violence detection. By analyzing the applications and
anomalies present in the datasets discussed across Section 2 to 7, we can see
that they mainly have specific types of anomalies. There are very few datasets
of heterogeneous nature (listed in Section 2.1). This is almost zero in audio-
visual category. This arises a strong need to develop datasets having diverse
ranges of anomalous samples. Also, the datasets in audio-visual category
mainly contain acted anomalies which limits its usefulness. Even if some acted
situation/events needs to be added, it should in such a manner that it appears
natural and contains the necessary amount of variations mimicking the real
life.

Apart from this, all the existing multimedia datasets lack anomalies with
concept drift. If an event/object is regarded anomaly in a dataset, it is
always regarded as an anomaly, no matter how frequent it may occur in distant
future. This is due to the fact that datasets are too short to contain this
effect. We should pay attention that the datasets which are more than a
duration of 5-10 hours are not suitable here because the samples are collected
from different time-stamp, location, and have only specific anomalies in rare
amount. Thus, shift of a sample from abnormal class into normal class is not
observed. These existing datasets are useful for specific anomaly detection,
particularly crime oriented anomaly detection, traffic rule violation etc.
However, generic scene monitoring, where raising an alarm for interesting
events which may not always be crime oriented, is demanded for future
applications like smart city surveillance. There are attempts to record long
untrimmed footage at one place, e.g., QMUL, ADOC, etc., however, the authors
do not attempt to provide annotations in accordance with concept drift.

##  9 Conclusion

This paper presents a survey of multimedia datasets for anomaly detection to
researchers working toward automated surveillance. The structured comparison
of datasets on various attributes also helps to understand the datasets
better. There is a large number of short-length and giant video datasets
available. Some are developed for generic scene surveillance, whereas some are
specific anomaly datasets. The number of datasets for the heterogeneous
anomaly is far less compared to specific anomaly datasets. There are too many
additional video datasets discussed which are not annotated or used for
anomaly but can be re-used to create bigger datasets. In the case of audio,
datasets are mainly developed for machine surveillance such as defect
detection, fault detection, etc. Generally, surveillance using audio alone in
outdoor scenes is not efficient due to the presence of multiple auditory
signals superimposed together. However, when they are analyzed together with
video, they can offer crucial and complementary information about the target
scene. But the datasets developed towards audio-visual surveillance are far
less compared to that for audio or video. There are a few audio-visual
datasets for a specific action such as fight, agitation detection. A recently
released dataset, namely the EMOLY dataset, has used only the upper body of
individuals and their speech information to facilitate abnormal behavior
detection. However, there is a strong need to develop more audio-visual
datasets for generic scene surveillance. We believe the survey presented in
this article will help the prospective researchers who intend to contribute
datasets or research in this field.

## References

  * [1] Varun Chandola, Arindam Banerjee, and Vipin Kumar,  “Anomaly detection: A survey,”  ACM computing surveys (CSUR), vol. 41, no. 3, pp. 1–58, 2009.
  * [2] Ruohan Gao, Tae-Hyun Oh, Kristen Grauman, and Lorenzo Torresani,  “Listen to look: Action recognition by previewing audio,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 2020, IEEE, pp. 10457–10467.
  * [3] N Patil and Prabir Kumar Biswas,  “A survey of video datasets for anomaly detection in automated surveillance,”  in 2016 Sixth International Symposium on Embedded Computing and System Design (ISED). IEEE, 2016, pp. 43–48.
  * [4] Vijay Mahadevan, Weixin Li, Viral Bhalodia, and Nuno Vasconcelos,  “Anomaly detection in crowded scenes,”  in 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. IEEE, 2010, pp. 1975–1981.
  * [5] Cewu Lu, Jianping Shi, and Jiaya Jia,  “Abnormal event detection at 150 fps in matlab,”  in Proceedings of the IEEE international conference on computer vision, 2013.
  * [6] ARENA,  “Dataset, pets,” 2014.
  * [7] Weixin Luo, Wen Liu, and Shenghua Gao,  “A revisit of sparse coding based anomaly detection in stacked rnn framework,”  in Proceedings of the IEEE International Conference on Computer Vision, 2017, pp. 341–349.
  * [8] Roberto Leyva, Victor Sanchez, and Chang-Tsun Li,  “The lv dataset: A realistic surveillance video dataset for abnormal event detection,”  in 2017 5th International Workshop on Biometrics and Forensics (IWBF). IEEE, 2017, pp. 1–6.
  * [9] Waqas Sultani, Chen Chen, and Mubarak Shah,  “Real-world anomaly detection in surveillance videos,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018.
  * [10] Bharathkumar Ramachandra and Michael Jones,  “Street scene: A new dataset and evaluation protocol for video anomaly detection,”  in Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2020, pp. 2569–2578.
  * [11] Mantini Pranav, Li Zhenggang, et al.,  “A day on campus-an anomaly detection dataset for events in a single camera,”  in Proceedings of the Asian Conference on Computer Vision, 2020\.
  * [12] Pierre-Marc Jodoin, Janusz Konrad, and Venkatesh Saligrama,  “Modeling background activity for behavior subtraction,”  in 2008 Second ACM/IEEE International Conference on Distributed Smart Cameras. IEEE, 2008, pp. 1–10.
  * [13] Amit Adam, Ehud Rivlin, Ilan Shimshoni, and Daviv Reinitz,  “Robust real-time unusual event detection using multiple fixed-location monitors,”  IEEE transactions on pattern analysis and machine intelligence, vol. 30, 2008.
  * [14] Ramin Mehran, Alexis Oyama, and Mubarak Shah,  “Abnormal crowd behavior detection using social force model,”  in 2009 IEEE Conference on Computer Vision and Pattern Recognition, 2009.
  * [15] J Ferryman and A Shahrokni,  “An overview of the pets 2009 challenge,”  Eleventh IEEE International Workshop on Performance Evaluation of Tracking and Surveillance, 2009.
  * [16] Andrei Zaharescu and Richard Wildes,  “Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing,”  in European Conference on Computer Vision. Springer, 2010, pp. 563–576.
  * [17] Mikel Rodriguez, Josef Sivic, Ivan Laptev, and Jean-Yves Audibert,  “Data-driven crowd analysis in videos,”  in 2011 International Conference on Computer Vision. IEEE, 2011, pp. 1235–1242.
  * [18] Enrique Bermejo Nievas, Oscar Deniz Suarez, Gloria Bueno García, and Rahul Sukthankar,  “Violence detection in video using computer vision techniques,”  in International conference on Computer analysis of images and patterns. Springer, 2011, pp. 332–339.
  * [19] Berkan Solmaz, Brian E Moore, and Mubarak Shah,  “Identifying behaviors in crowd scenes using stability analysis for dynamical systems,”  IEEE transactions on pattern analysis and machine intelligence, vol. 34, no. 10, pp. 2064–2070, 2012.
  * [20] Tal Hassner, Yossi Itcher, and Orit Kliper-Gross,  “Violent flows: Real-time detection of violent crowd behavior,”  in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops. IEEE, 2012, pp. 1–6.
  * [21] Bolei Zhou, Xiaogang Wang, and Xiaoou Tang,  “Understanding collective crowd behaviors: Learning a mixture model of dynamic pedestrian-agents,”  in 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012, pp. 2871–2878.
  * [22] Pierre Allain, Nicolas Courty, and Thomas Corpetti,  “Agoraset: a dataset for crowd video analysis,”  in 1st ICPR international workshop on pattern recognition and crowd analysis, 2012, pp. 1–6.
  * [23] Dirk Helbing, Anders Johansson, and Habib Zein Al-Abideen,  “Dynamics of crowd disasters: An empirical study,”  Physical review E, vol. 75, no. 4, pp. 046109, 2007.
  * [24] Thomas Driemeyer,  Rendering with mental ray®, vol. 1,  Springer, 2013.
  * [25] Pierre-Marc Jodoin, Yannick Benezeth, and Yi Wang,  “Meta-tracking for video scene understanding,”  in 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. IEEE, 2013, pp. 1–6.
  * [26] Shuai Yi, Hongsheng Li, and Xiaogang Wang,  “Understanding pedestrian behaviors from stationary crowd groups,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3488–3496.
  * [27] Paolo Rota, Nicola Conci, Nicu Sebe, and James M Rehg,  “Real-life violent social interaction detection,”  in 2015 IEEE International Conference on Image Processing (ICIP). IEEE, 2015, pp. 3456–3460.
  * [28] Hamidreza Rabiee, Javad Haddadnia, Hossein Mousavi, Maziyar Kalantarzadeh, Moin Nabi, and Vittorio Murino,  “Novel dataset for fine-grained abnormal behavior understanding in crowd,”  in 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance, 2016.
  * [29] Mauricio Perez, Alex C Kot, and Anderson Rocha,  “Detection of real-world fights in surveillance videos,”  in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 2662–2666.
  * [30] i Lids,  “dataset for avss,” 2007.
  * [31] Chen Change Loy, Tao Xiang, and Shaogang Gong,  “From local temporal correlation to global anomaly detection,”  in The 1st International Workshop on Machine Learning for Vision-based Motion Analysis-MLVMA’08, 2008.
  * [32] Yannick Benezeth, P-M Jodoin, Venkatesh Saligrama, and Christophe Rosenberger,  “Abnormal events detection based on spatio-temporal co-occurences,”  in 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009, pp. 2458–2465.
  * [33] Jagannadan Varadarajan and Jean-Marc Odobez,  “Topic models for scene analysis and abnormality detection,”  in 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops. IEEE, 2009, pp. 1338–1345.
  * [34] Dinesh Singh and Chalavadi Krishna Mohan,  “Deep spatio-temporal representation for detection of road accidents using stacked autoencoder,”  IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 879–887, 2018.
  * [35] Harpreet Singh, Emily M Hand, and Kostas Alexis,  “Anomalous motion detection on highway using deep learning,”  in 2020 IEEE International Conference on Image Processing (ICIP). IEEE, 2020, pp. 1901–1905.
  * [36] Fisher Yu, Haofeng Chen, Xin Wang, Wenqi Xian, Yingying Chen, Fangchen Liu, Vashisht Madhavan, and Trevor Darrell,  “Bdd100k: A diverse driving dataset for heterogeneous multitask learning,”  in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 2020, pp. 2636–2645.
  * [37] NVIDIA,  “Ai city,” 2021.
  * [38] Robert B Fisher,  “The pets04 surveillance ground-truth data sets,”  in Proc. 6th IEEE international workshop on performance evaluation of tracking and surveillance, 2004, pp. 1–5.
  * [39] Xiaogang Wang, Xiaoxu Ma, and W Eric L Grimson,  “Unsupervised activity perception in crowded and complicated scenes using hierarchical bayesian models,”  IEEE Transactions on pattern analysis and machine intelligence, vol. 31, no. 3, pp. 539–555, 2008.
  * [40] Scott Blunsden and RB Fisher,  “The behave video dataset: ground truthed video for multi-person behavior classification,”  Annals of the BMVA, vol. 4, no. 1-12, pp. 4, 2010.
  * [41] Sangmin Oh, Anthony Hoogs, Amitha Perera, Naresh Cuntoor, Chia-Chih Chen, Jong Taek Lee, Saurajit Mukherjee, JK Aggarwal, Hyungtae Lee, Larry Davis, et al.,  “A large-scale benchmark dataset for event recognition in surveillance video,”  in CVPR 2011. IEEE, 2011, pp. 3153–3160.
  * [42] Meng Wang, Wei Li, and Xiaogang Wang,  “Transferring a generic pedestrian detector towards specific scenes,”  in 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2012, pp. 3274–3281.
  * [43] Ke Chen, Chen Change Loy, Shaogang Gong, and Tony Xiang,  “Feature mining for localised crowd counting.,”  in Bmvc, 2012, p. 3.
  * [44] Bolei Zhou, Xiaoou Tang, and Xiaogang Wang,  “Measuring crowd collectiveness,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 3049–3056.
  * [45] Jing Shao, Chen Change Loy, and Xiaogang Wang,  “Scene-independent group profiling in crowd,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2014, pp. 2219–2226.
  * [46] Francesco Solera, Simone Calderara, and Rita Cucchiara,  “Learning to identify leaders in crowd,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2015, pp. 43–48.
  * [47] Cong Zhang, Hongsheng Li, Xiaogang Wang, and Xiaokang Yang,  “Cross-scene crowd counting via deep convolutional neural networks,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2015, pp. 833–841.
  * [48] Federico Bartoli, Giuseppe Lisanti, Lorenzo Seidenari, Svebor Karaman, and Alberto Del Bimbo,  “Museumvisitors: a dataset for pedestrian and group detection, gaze estimation and behavior understanding,”  in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2015, pp. 19–27.
  * [49] Davide Conigliaro, Paolo Rota, Francesco Setti, Chiara Bassetti, Nicola Conci, Nicu Sebe, and Marco Cristani,  “The s-hock dataset: Analyzing crowds at the stadium,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 2039–2047.
  * [50] Jing Shao, Chen Change Loy, Kai Kang, and Xiaogang Wang,  “Crowded scene understanding by deeply learned volumetric slices,”  IEEE transactions on circuits and systems for video technology, vol. 27, no. 3, pp. 613–623, 2016.
  * [51] Camille Dupont, Luis Tobias, and Bertrand Luvison,  “Crowd-11: A dataset for fine grained crowd behaviour analysis,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, 2017, pp. 9–16.
  * [52] Sergio A Velastin and Diego A Gómez-Lira,  “People detection and pose classification inside a moving train using computer vision,”  in International Visual Informatics Conference. Springer, 2017, pp. 319–330.
  * [53] Chen Change Loy, Ke Chen, Shaogang Gong, and Tao Xiang,  “Crowd counting and profiling: Methodology and evaluation,”  in Modeling, simulation and visual analysis of crowds, pp. 347–382. Springer, 2013.
  * [54] Tianchun Xu, Xiaohui Chen, Guo Wei, and Weidong Wang,  “Crowd counting using accumulated hog,”  in 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD). IEEE, 2016, pp. 1877–1881.
  * [55] Mahdyar Ravanbakhsh, Moin Nabi, Hossein Mousavi, Enver Sangineto, and Nicu Sebe,  “Plug-and-play cnn for crowd motion analysis: An application in abnormal event detection,”  in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018, pp. 1689–1698.
  * [56] Yang Cong, Junsong Yuan, and Ji Liu,  “Abnormal event detection in crowded scenes using sparse representation,”  Pattern Recognition, vol. 46, no. 7, pp. 1851–1864, 2013.
  * [57] Viet-Quoc Pham, Tatsuo Kozakaya, Osamu Yamaguchi, and Ryuzo Okada,  “Count forest: Co-voting uncertain number of targets using random forest for crowd density estimation,”  in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3253–3261.
  * [58] Mohammad Sabokrou, Mohsen Fayyaz, Mahmood Fathy, Zahra Moayed, and Reinhard Klette,  “Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes,”  Computer Vision and Image Understanding, vol. 172, pp. 88–97, 2018\.
  * [59] Dan Xu, Yan Yan, Elisa Ricci, and Nicu Sebe,  “Detecting anomalous events in videos by learning deep representations of appearance and motion,”  Computer Vision and Image Understanding, vol. 156, pp. 117–127, 2017.
  * [60] Jia-Xing Zhong, Nannan Li, Weijie Kong, Shan Liu, Thomas H Li, and Ge Li,  “Graph convolutional label noise cleaner: Train a plug-and-play action classifier for anomaly detection,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2019, pp. 1237–1246.
  * [61] Dong Gong, Lingqiao Liu, Vuong Le, Budhaditya Saha, Moussa Reda Mansour, Svetha Venkatesh, and Anton van den Hengel,  “Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection,”  in Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019, pp. 1705–1714.
  * [62] Mahmudul Hasan, Jonghyun Choi, Jan Neumann, Amit K Roy-Chowdhury, and Larry S Davis,  “Learning temporal regularity in video sequences,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 733–742.
  * [63] Luis Patino and James Ferryman,  “Multiresolution semantic activity characterisation and abnormality discovery in videos,”  Applied Soft Computing, vol. 25, pp. 485–495, 2014.
  * [64] Luis Patino and James Ferryman,  “Detecting threat behaviours,”  in 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2016, pp. 88–94.
  * [65] Gertjan J Burghouts, Paulien van Slingerland, RJM ten Hove, Richard JM den Hollander, and Klamer Schutte,  “Complex threat detection: Learning vs. rules, using a hierarchy of features,”  in 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2014, pp. 375–380.
  * [66] Vahid Bastani, Damian Campo, Lucio Marcenaro, and Carlo Regazzoni,  “Online pedestrian group walking event detection using spectral analysis of motion similarity graph,”  in 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2015, pp. 1–5.
  * [67] Hai-Wen Chen and Mike McGurr,  “Improved color and intensity patch segmentation for human full-body and body-parts detection and tracking,”  in 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2014, pp. 361–368.
  * [68] Wen Liu, Weixin Luo, Dongze Lian, and Shenghua Gao,  “Future frame prediction for anomaly detection–a new baseline,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2018, pp. 6536–6545.
  * [69] Vishwanath A Sindagi and Vishal M Patel,  “Cnn-based cascaded multi-task learning of high-level prior and density estimation for crowd counting,”  in 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2017, pp. 1–6.
  * [70] Muhammad Umar Karim Khan, Hyun-Sang Park, and Chong-Min Kyung,  “Rejecting motion outliers for efficient crowd anomaly detection,”  IEEE Transactions on Information Forensics and Security, vol. 14, no. 2, pp. 541–556, 2018.
  * [71] Roberto Leyva, Victor Sanchez, and Chang-Tsun Li,  “Abnormal event detection in videos using binary features,”  in 2017 40th International Conference on Telecommunications and Signal Processing (TSP). IEEE, 2017, pp. 621–625.
  * [72] K Deepak, S Chandrakala, and C Krishna Mohan,  “Residual spatiotemporal autoencoder for unsupervised video anomaly detection,”  Signal, Image and Video Processing, vol. 15, no. 1, pp. 215–222, 2021.
  * [73] Michael George, CV Bijitha, and Babita Roslind Jose,  “Crowd panic detection using autoencoder with non-uniform feature extraction,”  in 2018 8th International Symposium on Embedded Computing and System Design (ISED). IEEE, 2018, pp. 11–15.
  * [74] Snehashis Majhi, Ratnakar Dash, and Pankaj Kumar Sa,  “Temporal pooling in inflated 3dcnn for weakly-supervised video anomaly detection,”  in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 2020, pp. 1–6.
  * [75] Waseem Ullah, Amin Ullah, Ijaz Ul Haq, Khan Muhammad, Muhammad Sajjad, and Sung Wook Baik,  “Cnn features with bi-directional lstm for real-time anomaly detection in surveillance networks,”  Multimedia Tools and Applications, vol. 80, no. 11, pp. 16979–16995, 2021.
  * [76] Masoud Pourreza, Mohammadreza Salehi, and Mohammad Sabokrou,  “Ano-graph: Learning normal scene contextual graphs to detect video anomalies,”  arXiv preprint arXiv:2103.10502, 2021.
  * [77] Supriya Kapoor and Rahul Bhatia,  Intelligent Systems and Applications: Proceedings of the 2020 Intelligent Systems Conference (IntelliSys).. Volume 1, vol. 1250,  Springer Nature, 2020.
  * [78] Fernando P dos Santos, Leonardo SF Ribeiro, and Moacir A Ponti,  “Generalization of feature embeddings transferred from different video anomaly detection domains,”  Journal of Visual Communication and Image Representation, vol. 60, pp. 407–416, 2019.
  * [79] Yang Cong, Junsong Yuan, and Ji Liu,  “Sparse reconstruction cost for abnormal event detection,”  in CVPR 2011. IEEE, 2011, pp. 3449–3456.
  * [80] Louis Kratz and Ko Nishino,  “Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models,”  in 2009 IEEE conference on computer vision and pattern recognition. IEEE, 2009, pp. 1446–1453.
  * [81] Oluwatoyin P Popoola and Kejun Wang,  “Video-based abnormal human behavior recognition—a review,”  IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 42, no. 6, pp. 865–878, 2012.
  * [82] Mehrsan Javan Roshtkhari and Martin D Levine,  “Online dominant and anomalous behavior detection in videos,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2013, pp. 2611–2618.
  * [83] Hossein Mousavi, Sadegh Mohammadi, Alessandro Perina, Ryad Chellali, and Vittorio Murino,  “Analyzing tracklets for the detection of abnormal crowd behavior,”  in 2015 IEEE Winter Conference on Applications of Computer Vision. IEEE, 2015, pp. 148–155.
  * [84] Hang Su, Hua Yang, Shibao Zheng, Yawen Fan, and Sha Wei,  “The large-scale crowd behavior perception based on spatio-temporal viscous fluid field,”  IEEE Transactions on Information Forensics and security, vol. 8, no. 10, pp. 1575–1589, 2013.
  * [85] Mingliang Xu, Chunxu Li, Pei Lv, Nie Lin, Rui Hou, and Bing Zhou,  “An efficient method of crowd aggregation computation in public areas,”  IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 10, pp. 2814–2825, 2017.
  * [86] Si Wu, Hau-San Wong, and Zhiwen Yu,  “A bayesian model for crowd escape behavior detection,”  IEEE transactions on circuits and systems for video technology, vol. 24, no. 1, pp. 85–98, 2013.
  * [87] Xiaobin Zhu, Jing Liu, Jinqiao Wang, Changsheng Li, and Hanqing Lu,  “Sparse representation for robust abnormality detection in crowded scenes,”  Pattern Recognition, vol. 47, no. 5, pp. 1791–1799, 2014.
  * [88] Laura Leal-Taixé, Gerard Pons-Moll, and Bodo Rosenhahn,  “Everybody needs somebody: Modeling social and grouping behavior on a linear programming multiple people tracker,”  in 2011 IEEE international conference on computer vision workshops (ICCV workshops). IEEE, 2011, pp. 120–127.
  * [89] Chun-Te Chu, Jenq-Neng Hwang, Hung-I Pai, and Kung-Ming Lan,  “Tracking human under occlusion based on adaptive multiple kernels with projected gradients,”  IEEE Transactions on Multimedia, vol. 15, no. 7, pp. 1602–1615, 2013.
  * [90] Hajer Fradi and Jean-Luc Dugelay,  “Towards crowd density-aware video surveillance applications,”  Information Fusion, vol. 24, pp. 3–15, 2015.
  * [91] David S Bolme, Yui Man Lui, Bruce A Draper, and J Ross Beveridge,  “Simple real-time human detection using a single correlation filter,”  in 2009 Twelfth IEEE International Workshop on Performance Evaluation of Tracking and Surveillance. IEEE, 2009, pp. 1–8.
  * [92] Cristina Conde, Daniela Moctezuma, Isaac Martin De Diego, and Enrique Cabello,  “Hogg: Gabor and hog-based human detection for surveillance in non-controlled environments,”  Neurocomputing, vol. 100, pp. 19–30, 2013.
  * [93] Jun Yang, Zhongke Shi, and Patricio A Vela,  “Person reidentification by kernel pca based appearance learning,”  in 2011 Canadian Conference on Computer and Robot Vision. IEEE, 2011, pp. 227–233.
  * [94] Tan Xiao, Chao Zhang, and Hongbin Zha,  “Learning to detect anomalies in surveillance video,”  IEEE Signal Processing Letters, vol. 22, no. 9, pp. 1477–1481, 2015\.
  * [95] Mehrsan Javan Roshtkhari and Martin D Levine,  “An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions,”  Computer vision and image understanding, vol. 117, no. 10, pp. 1436–1452, 2013.
  * [96] Kinjal V Joshi and Narendra M Patel,  “A cnn based approach for crowd anomaly detection.,”  International Journal of Next-Generation Computing, vol. 12, no. 1, 2021.
  * [97] Mei Kuan Lim, Ven Jyn Kok, Chen Change Loy, and Chee Seng Chan,  “Crowd saliency detection via global similarity structure,”  in 2014 22nd International Conference on Pattern Recognition. IEEE, 2014, pp. 3957–3962.
  * [98] Aniket Bera, Sujeong Kim, and Dinesh Manocha,  “Realtime anomaly detection using trajectory-level crowd behavior learning,”  in Proceedings of the IEEE conference on computer vision and pattern recognition workshops, 2016, pp. 50–57.
  * [99] Ven Jyn Kok and Chee Seng Chan,  “Grcs: Granular computing-based crowd segmentation,”  IEEE transactions on cybernetics, vol. 47, no. 5, pp. 1157–1168, 2016.
  * [100] Wei Song, Dongliang Zhang, Xiaobing Zhao, Jing Yu, Rui Zheng, and Antai Wang,  “A novel violent video detection scheme based on modified 3d convolutional neural networks,”  IEEE Access, vol. 7, pp. 39172–39179, 2019.
  * [101] Samee Ullah Khan, Ijaz Ul Haq, Seungmin Rho, Sung Wook Baik, and Mi Young Lee,  “Cover the violence: A novel deep-learning-based approach towards violence-detection in movies,”  Applied Sciences, vol. 9, no. 22, pp. 4963, 2019.
  * [102] Xingyu Xu, Xiaoyu Wu, Ge Wang, and Huimin Wang,  “Violent video classification based on spatial-temporal cues using deep learning,”  in 2018 11th International Symposium on Computational Intelligence and Design (ISCID). IEEE, 2018, vol. 1, pp. 319–322.
  * [103] Ming Cheng, Kunjing Cai, and Ming Li,  “Rwf-2000: An open large scale video database for violence detection,”  in 2020 25th International Conference on Pattern Recognition (ICPR). IEEE, 2021, pp. 4183–4190.
  * [104] Yuan Gao, Hong Liu, Xiaohu Sun, Can Wang, and Yi Liu,  “Violence detection using oriented violent flows,”  Image and vision computing, vol. 48, pp. 37–41, 2016.
  * [105] Yanhao Zhang, Lei Qin, Rongrong Ji, Hongxun Yao, and Qingming Huang,  “Social attribute-aware force model: exploiting richness of interaction for abnormal crowd detection,”  IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 7, pp. 1231–1245, 2014.
  * [106] Jijia Li, Hua Yang, and Shuang Wu,  “Crowd semantic segmentation based on spatial-temporal dynamics,”  in 2016 13th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2016, pp. 102–108.
  * [107] Yanyu Xu, Zhixin Piao, and Shenghua Gao,  “Encoding crowd interaction with deep neural network for pedestrian trajectory prediction,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2018, pp. 5275–5284.
  * [108] Shuai Yi, Hongsheng Li, and Xiaogang Wang,  “Pedestrian behavior modeling from stationary crowds with applications to intelligent surveillance,”  IEEE transactions on image processing, vol. 25, no. 9, pp. 4354–4368, 2016.
  * [109] Bolei Zhou, Xiaoou Tang, and Xiaogang Wang,  “Learning collective crowd behaviors with dynamic pedestrian-agents,”  International Journal of Computer Vision, vol. 111, no. 1, pp. 50–68, 2015.
  * [110] Jinghui Zhong, Wentong Cai, Linbo Luo, and Haiyan Yin,  “Learning behavior patterns from video: A data-driven framework for agent-based crowd modeling,”  in Proceedings of the 2015 International Conference on Autonomous Agents and Multiagent Systems, 2015, pp. 801–809.
  * [111] Shayan Modiri Assari, Haroon Idrees, and Mubarak Shah,  “Human re-identification in crowd videos using personal, social and environmental constraints,”  in European Conference on Computer Vision. Springer, 2016, pp. 119–136.
  * [112] Kelathodi Kumaran Santhosh, Debi Prosad Dogra, Partha Pratim Roy, and Bidyut Baran Chaudhuri,  “Trajectory-based scene understanding using dirichlet process mixture model,”  IEEE transactions on cybernetics, 2019.
  * [113] Andrii Maksai, Xinchao Wang, Francois Fleuret, and Pascal Fua,  “Non-markovian globally consistent multi-object tracking,”  in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2544–2554.
  * [114] M Ayyüce Kızrak Sağun and Bülent Bolat,  “A novel approach for people counting and tracking from crowd video,”  in 2017 IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA). IEEE, 2017, pp. 277–281.
  * [115] Doaa Shehab and Heyfa Ammar,  “Statistical detection of a panic behavior in crowded scenes,”  Machine Vision and Applications, vol. 30, no. 5, pp. 919–931, 2019\.
  * [116] Antoine Fagette, Patrick Jamet, Daniel Racoceanu, and Jean-Yves Dufour,  “Particle video for crowd flow tracking,”  in Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods, 2014, pp. 445–452.
  * [117] Antoine Basset, Patrick Bouthemy, and Charles Kervrann,  “Frame-by-frame crowd motion classification from affine motion models,”  in 2013 10th IEEE International Conference on Advanced Video and Signal Based Surveillance. IEEE, 2013, pp. 282–287.
  * [118] Andrea Pennisi, Domenico D Bloisi, and Luca Iocchi,  “Online real-time crowd behavior detection in video sequences,”  Computer Vision and Image Understanding, vol. 144, pp. 166–176, 2016.
  * [119] Xuelong Li, Mulin Chen, and Qi Wang,  “Measuring collectiveness via refined topological similarity,”  ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), vol. 12, no. 2, pp. 1–22, 2016.
  * [120] Sujeong Kim, Aniket Bera, and Dinesh Manocha,  “Interactive crowd content generation and analysis using trajectory-level behavior learning,”  in 2015 IEEE International Symposium on Multimedia (ISM). IEEE, 2015, pp. 21–26.
  * [121] Zheyi Fan, Jiao Jiang, Shuqin Weng, Zhonghang He, and Zhiwen Liu,  “Adaptive crowd segmentation based on coherent motion detection,”  Journal of Signal Processing Systems, vol. 90, no. 12, pp. 1651–1666, 2018.
  * [122] Tharindu Fernando, Simon Denman, Sridha Sridharan, and Clinton Fookes,  “Soft+ hardwired attention: An lstm framework for human trajectory prediction and abnormal event detection,”  Neural networks, vol. 108, pp. 466–478, 2018.
  * [123] Shuai Yi, Hongsheng Li, and Xiaogang Wang,  “Pedestrian travel time estimation in crowded scenes,”  in Proceedings of the IEEE International Conference on Computer Vision, 2015, pp. 3137–3145.
  * [124] Yuke Li,  “A deep spatiotemporal perspective for understanding crowd behavior,”  IEEE Transactions on multimedia, vol. 20, no. 12, pp. 3289–3297, 2018.
  * [125] Lazaros Lazaridis, Anastasios Dimou, and Petros Daras,  “Abnormal behavior detection in crowded scenes using density heatmaps and optical flow,”  in 2018 26th European Signal Processing Conference (EUSIPCO). IEEE, 2018, pp. 2060–2064.
  * [126] Heyfa Ammar and Asma Cherif,  “Deeprod: A deep learning approach for real-time and online detection of a panic behavior in human crowds,”  Machine Vision and Applications, vol. 32, no. 3, pp. 1–15, 2021\.
  * [127] Şeymanur Aktı, Gözde Ayşe Tataroğlu, and Hazım Kemal Ekenel,  “Vision-based fight detection from surveillance cameras,”  in 2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA). IEEE, 2019, pp. 1–6.
  * [128] Qiujie Li, Yaobin Mao, Zhiquan Wang, and Wenbo Xiang,  “Robust real-time detection of abandoned and removed objects,”  in 2009 Fifth International Conference on Image and Graphics. IEEE, 2009, pp. 156–161.
  * [129] Xinyi Cui, Qingshan Liu, Mingchen Gao, and Dimitris N Metaxas,  “Abnormal detection using interaction energy potentials,”  in CVPR 2011. IEEE, 2011, pp. 3161–3167.
  * [130] Kelathodi Kumaran Santhosh, Debi Prosad Dogra, Partha Pratim Roy, and Adway Mitra,  “Vehicular trajectory classification and traffic anomaly detection in videos using a hybrid cnn-vae architecture,”  IEEE Transactions on Intelligent Transportation Systems, 2021.
  * [131] Chen Change Loy, Tao Xiang, and Shaogang Gong,  “Detecting and discriminating behavioural anomalies,”  Pattern Recognition, vol. 44, no. 1, pp. 117–132, 2011.
  * [132] Jagannadan Varadarajan, Ramanathan Subramanian, Narendra Ahuja, Pierre Moulin, and Jean-Marc Odobez,  “Active online anomaly detection using dirichlet process mixture model and gaussian process classification,”  in 2017 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2017, pp. 615–623.
  * [133] Vagia Kaltsa, Alexia Briassouli, Ioannis Kompatsiaris, and Michael G Strintzis,  “Multiple hierarchical dirichlet processes for anomaly detection in traffic,”  Computer Vision and Image Understanding, vol. 169, pp. 28–39, 2018\.
  * [134] Shifu Zhou, Wei Shen, Dan Zeng, Mei Fang, Yuanwang Wei, and Zhijiang Zhang,  “Spatial–temporal convolutional neural networks for anomaly detection and localization in crowded scenes,”  Signal Processing: Image Communication, vol. 47, pp. 358–368, 2016\.
  * [135] Yannick Benezeth, Pierre-Marc Jodoin, and Venkatesh Saligrama,  “Abnormality detection using low-level co-occurring events,”  Pattern Recognition Letters, vol. 32, no. 3, pp. 423–431, 2011\.
  * [136] Weixin Li, Vijay Mahadevan, and Nuno Vasconcelos,  “Anomaly detection and localization in crowded scenes,”  IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 1, pp. 18–32, 2013.
  * [137] Jagannadan Varadarajan, Rémi Emonet, and Jean-Marc Odobez,  “A sequential topic model for mining recurrent activities from long term video logs,”  International journal of computer vision, vol. 103, no. 1, pp. 100–126, 2013.
  * [138] Yucheng Chen, Longlong Jing, Elahe Vahdani, Ling Zhang, Mingyi He, and Yingli Tian,  “Multi-camera vehicle tracking and re-identification on ai city challenge 2019.,”  in CVPR Workshops, 2019, vol. 2.
  * [139] Armstrong Aboah,  “A vision-based system for traffic anomaly detection using deep learning and decision trees,”  in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2021, pp. 4207–4212.
  * [140] Jinsol Ha, Jinho Park, Heegwang Kim, Hasil Park, and Joonki Paik,  “Violence detection for video surveillance system using irregular motion information,”  in 2018 International Conference on Electronics, Information, and Communication (ICEIC). IEEE, 2018, pp. 1–3.
  * [141] Pablo Negri and Pablo Lotito,  “Pedestrian detection on caviar dataset using a movement feature space,”  in Proceedings of the 13th Argentine Symposium on Technology. Citeseer, 2012, pp. 1850–2806.
  * [142] Peixi Peng, Tao Xiang, Yaowei Wang, Massimiliano Pontil, Shaogang Gong, Tiejun Huang, and Yonghong Tian,  “Unsupervised cross-dataset transfer learning for person re-identification,”  in Proceedings of the IEEE conference on computer vision and pattern recognition, 2016, pp. 1306–1315.
  * [143] Rolando J Cárdenas, Cesar A Beltrán, and Juan C Gutiérrez,  “Small face detection using deep learning on surveillance videos,”  environment, vol. 2, no. 5, pp. 14, 2019.
  * [144] CV Amrutha, C Jyotsna, and J Amudha,  “Deep learning approach for suspicious activity detection from surveillance video,”  in 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE, 2020, pp. 335–339.
  * [145] Cheng Chen, Alexandre Heili, and Jean-Marc Odobez,  “Combined estimation of location and body pose in surveillance video,”  in 2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2011, pp. 5–10.
  * [146] Mujtaba Asad, Jie Yang, Jiang He, Pourya Shamsolmoali, and Xiangjian He,  “Multi-frame feature-fusion-based model for violence detection,”  The Visual Computer, vol. 37, no. 6, pp. 1415–1431, 2021.
  * [147] Kris M Kitani, Brian D Ziebart, James Andrew Bagnell, and Martial Hebert,  “Activity forecasting,”  in European conference on computer vision. Springer, 2012, pp. 201–214.
  * [148] Shu Zhang, Yingying Zhu, and Amit K Roy-Chowdhury,  “Context-aware surveillance video summarization,”  IEEE Transactions on Image Processing, vol. 25, no. 11, pp. 5469–5478, 2016.
  * [149] Xudong Li, Mao Ye, Yiguang Liu, Feng Zhang, Dan Liu, and Song Tang,  “Accurate object detection using memory-based models in surveillance scenes,”  Pattern Recognition, vol. 67, pp. 73–84, 2017.
  * [150] Weiyao Lin, Yang Mi, Weiyue Wang, Jianxin Wu, Jingdong Wang, and Tao Mei,  “A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes,”  IEEE Transactions on Image Processing, vol. 25, no. 4, pp. 1674–1687, 2016.
  * [151] Shuang Wu, Hua Yang, Shibao Zheng, Hang Su, Yawen Fan, and Ming-Hsuan Yang,  “Crowd behavior analysis via curl and divergence of motion trajectories,”  International Journal of Computer Vision, vol. 123, no. 3, pp. 499–519, 2017.
  * [152] Chong Shang, Haizhou Ai, and Bo Bai,  “End-to-end crowd counting via joint learning local and global count,”  in 2016 IEEE International Conference on Image Processing (ICIP). IEEE, 2016, pp. 1215–1219.
  * [153] Federico Bartoli, Giuseppe Lisanti, Lorenzo Seidenari, and Alberto Del Bimbo,  “User interest profiling using tracking-free coarse gaze estimation,”  in 2016 23rd International Conference on Pattern Recognition (ICPR). IEEE, 2016, pp. 1839–1844.
  * [154] Sultan Daud Khan, Habib Ullah, Mohib Ullah, Nicola Conci, Faouzi Alaya Cheikh, and Azeddine Beghdadi,  “Person head detection based deep model for people counting in sports videos,”  in 2019 16th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2019, pp. 1–8.
  * [155] Francesco Setti, Davide Conigliaro, Michele Tobanelli, and Marco Cristani,  “Count on me: learning to count on a single image,”  IEEE Transactions on Circuits and Systems for Video Technology, vol. 28, no. 8, pp. 1798–1806, 2017.
  * [156] Yasir Jan, Ferdous Sohel, Mohd Fairuz Shiratuddin, and Kok Wai Wong,  “Wnet: Joint multiple head detection and head pose estimation from a spectator crowd image,”  in Asian Conference on Computer Vision. Springer, 2018, pp. 484–493.
  * [157] Vittorio Murino, Marco Cristani, Shishir Shah, and Silvio Savarese,  “The group and crowd analysis interdisciplinary challenge,”  in Group and Crowd Behavior for Computer Vision, pp. 1–11. Elsevier, 2017.
  * [158] Jing Shao, Chen-Change Loy, Kai Kang, and Xiaogang Wang,  “Slicing convolutional neural network for crowd video understanding,”  in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2016, pp. 5620–5628.
  * [159] Elizabeth B Varghese and Sabu M Thampi,  “Application of cognitive computing for smart crowd management,”  IT Professional, vol. 22, no. 4, pp. 43–50, 2020.
  * [160] Mounir Bendali-Braham, Jonathan Weber, Germain Forestier, Lhassane Idoumghar, and Pierre-Alain Muller,  “Transfer learning for the classification of video-recorded crowd movements,”  in 2019 11th International Symposium on Image and Signal Processing and Analysis (ISPA). IEEE, 2019, pp. 271–276.
  * [161] Jonathan Pan,  “Physical integrity attack detection of surveillance camera with deep learning based video frame interpolation,”  in 2019 IEEE International Conference on Internet of Things and Intelligence System (IoTaIS). IEEE, 2019, pp. 79–85.
  * [162] Pratibha Kumari and Mukesh Saini,  “Multivariate adaptive gaussian mixture for scene level anomaly modeling,”  in 2020 IEEE Sixth International Conference on Multimedia Big Data, New Delhi, India, 2020, pp. 54–62, IEEE.
  * [163] Pasquale Foggia, Alessia Saggese, Nicola Strisciuglio, and Mario Vento,  “Cascade classifiers trained on gammatonegrams for reliably detecting audio events,”  in 2014 11th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE, 2014, pp. 50–55.
  * [164] Pasquale Foggia, Nicolai Petkov, Alessia Saggese, Nicola Strisciuglio, and Mario Vento,  “Audio surveillance of roads: A system for detecting anomalous sounds,”  IEEE transactions on intelligent transportation systems, vol. 17, no. 1, pp. 279–288, 2015.
  * [165] Sarab S Sethi, Nick S Jones, Ben D Fulcher, Lorenzo Picinali, Dena Jane Clink, Holger Klinck, C David L Orme, Peter H Wrege, and Robert M Ewers,  “Characterizing soundscapes across diverse ecosystems using a universal acoustic feature set,”  Proceedings of the National Academy of Sciences, vol. 117, no. 29, pp. 17049–17055, 2020.
  * [166] Jort F. Gemmeke, Daniel P. W. Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R. Channing Moore, Manoj Plakal, and Marvin Ritter,  “Audio set: An ontology and human-labeled dataset for audio events,”  in Proc. IEEE ICASSP 2017, New Orleans, LA, 2017.
  * [167] Annamaria Mesaros, Toni Heittola, Aleksandr Diment, Benjamin Elizalde, Ankit Shah, Emmanuel Vincent, Bhiksha Raj, and Tuomas Virtanen,  “Dcase 2017 challenge setup: Tasks, datasets and baseline system,”  in DCASE 2017-Workshop on Detection and Classification of Acoustic Scenes and Events, 2017.
  * [168] Annamaria Mesaros, Toni Heittola, and Tuomas Virtanen,  “Tut database for acoustic scene classification and sound event detection,”  in 2016 24th European Signal Processing Conference (EUSIPCO). IEEE, 2016, pp. 1128–1132.
  * [169] Lam Pham, Ian McLoughlin, Huy Phan, Minh Tran, Truc Nguyen, and Ramaswamy Palaniappan,  “Robust deep learning framework for predicting respiratory anomalies and diseases,”  in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE, 2020, pp. 164–167.
  * [170] BM Rocha, Dimitris Filos, L Mendes, I Vogiatzis, E Perantoni, E Kaimakamis, P Natsiavas, Ana Oliveira, C Jácome, A Marques, et al.,  “A respiratory sound database for the development of automated classification,”  in International Conference on Biomedical and Health Informatics. Springer, 2017, pp. 33–37.
  * [171] Dong Yul Oh and Il Dong Yun,  “Residual error based anomaly detection using auto-encoder in smd machine sound,”  Sensors, vol. 18, no. 5, pp. 1308, 2018.
  * [172] Ryo Tanabe, Harsh Purohit, Kota Dohi, Takashi Endo, Yuki Nikaido, Toshiki Nakamura, and Yohei Kawaguchi,  “Mimii due: Sound dataset for malfunctioning industrial machine investigation and inspection with domain shifts due to changes in operational and environmental conditions,”  arXiv preprint arXiv:2105.02702, 2021.
  * [173] Yuma Koizumi, Shoichiro Saito, Hisashi Uematsu, Noboru Harada, and Keisuke Imoto,  “Toyadmos: A dataset of miniature-machine operating sounds for anomalous sound detection,”  in 2019 IEEE Workshop on Applications of Signal Processing to Audio and Acoustics (WASPAA). IEEE, 2019, pp. 313–317.
  * [174] Joan Claudi Socoró, Gerard Ribera, Xavier Sevillano, and Francesc Alías,  “Development of an anomalous noise event detection algorithm for dynamic road traffic noise mapping,”  in Proceedings of the 22nd International Congress on Sound and Vibration (ICSV22), Florence, Italy, 2015, pp. 12–16.
  * [175] Noboru Harada, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi, Masahiro Yasuda, and Shoichiro Saito,  “Toyadmos2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditions,”  arXiv preprint arXiv:2106.02369, 2021.
  * [176] Yohei Kawaguchi, Keisuke Imoto, Yuma Koizumi, Noboru Harada, Daisuke Niizumi, Kota Dohi, Ryo Tanabe, Harsh Purohit, and Takashi Endo,  “Description and discussion on dcase 2021 challenge task 2: Unsupervised anomalous sound detection for machine condition monitoring under domain shifted conditions,”  arXiv preprint arXiv:2106.04492, 2021.
  * [177] Nicola Strisciuglio, Mario Vento, and Nicolai Petkov,  “Learning representations of sound using trainable cope feature extractors,”  Pattern recognition, vol. 92, pp. 25–36, 2019.
  * [178] Noor Almaadeed, Muhammad Asim, Somaya Al-Maadeed, Ahmed Bouridane, and Azeddine Beghdadi,  “Automatic detection and classification of audio events for road surveillance applications,”  Sensors, vol. 18, no. 6, pp. 1858, 2018.
  * [179] Antonio Greco, Nicolai Petkov, Alessia Saggese, and Mario Vento,  “Aren: A deep learning approach for sound event recognition using a brain inspired representation,”  IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3610–3624, 2020.
  * [180] Oleksandr I Provotar, Yaroslav M Linder, and Maksym M Veres,  “Unsupervised anomaly detection in time series using lstm-based autoencoders,”  in 2019 IEEE International Conference on Advanced Trends in Information Theory (ATIT). IEEE, 2019, pp. 513–517.
  * [181] Ellen Rushe and Brian Mac Namee,  “Anomaly detection in raw audio using deep autoregressive networks,”  in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019, pp. 3597–3601.
  * [182] Hai Chen, Xiaochen Yuan, Zhiyuan Pei, Mianjie Li, and Jianqing Li,  “Triple-classification of respiratory sounds using optimized s-transform and deep residual networks,”  IEEE Access, vol. 7, pp. 32845–32852, 2019.
  * [183] Jyotibdha Acharya and Arindam Basu,  “Deep neural network for respiratory sound classification in wearable devices enabled by patient specific model tuning,”  IEEE transactions on biomedical circuits and systems, vol. 14, no. 3, pp. 535–544, 2020.
  * [184] Fatih Demir, Abdulkadir Sengur, and Varun Bajaj,  “Convolutional neural networks based efficient approach for classification of lung diseases,”  Health information science and systems, vol. 8, no. 1, pp. 1–8, 2020.
  * [185] Paul Primus, Verena Haunschmid, Patrick Praher, and Gerhard Widmer,  “Anomalous sound detection as a simple binary classification problem with careful selection of proxy outlier examples,”  arXiv preprint arXiv:2011.02949, 2020.
  * [186] Yuma Koizumi, Masahiro Yasuda, Shin Murata, Shoichiro Saito, Hisashi Uematsu, and Noboru Harada,  “Spidernet: Attention network for one-shot anomaly detection in sounds,”  in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020, pp. 281–285.
  * [187] Joan Claudi Socoró, Francesc Alías, and Rosa Ma Alsina-Pagès,  “An anomalous noise events detector for dynamic road traffic noise mapping in real-life urban and suburban environments,”  Sensors, vol. 17, no. 10, pp. 2323, 2017.
  * [188] Francesc Alías and Joan Claudi Socoró,  “Description of anomalous noise events for reliable dynamic traffic noise mapping in real-life urban and suburban soundscapes,”  Applied Sciences, vol. 7, no. 2, pp. 146, 2017.
  * [189] Kazuki Morita, Tomohiko Yano, and K Tran,  “Anomalous sound detection using cnn-based features by self supervised learning,”  Tech. Rep., DCASE2021 Challenge, Tech. Rep, 2021.
  * [190] Kevin Wilkinghoff,  “Utilizing sub-cluster adacos for anomalous sound detection under domain shifted conditions,”  Tech. Rep., DCASE2021 Challenge, Tech. Rep, 2021.
  * [191] Ibuki Kuroyanagi, Tomoki Hayashi, Yusuke Adachi, Takenori Yoshimura, Kazuya Takeda, and Tomoki Toda,  “Anomalous sound detection with ensemble of autoencoder and binary classification approaches,”  Tech. Rep., DCASE2021 Challenge, Tech. Rep, 2021.
  * [192] Iulia Lefter, Gertjan J Burghouts, and Leon JM Rothkrantz,  “An audio-visual dataset of human–human interactions in stressful situations,”  Journal on Multimodal User Interfaces, vol. 8, no. 1, pp. 29–41, 2014.
  * [193] Claire-Hélène Demarty, Cédric Penet, Mohammad Soleymani, and Guillaume Gravier,  “Vsd, a public dataset for the detection of violent scenes in movies: design, annotation, analysis and evaluation,”  Multimedia Tools and Applications, vol. 74, no. 17, pp. 7379–7404, 2015.
  * [194] Cédric Fayet, Arnaud Delhay, Damien Lolive, and Pierre-François Marteau,  “Emo&ly (emotion and anomaly): A new corpus for anomaly detection in an audiovisual stream with emotional context.,”  in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018.
  * [195] Peng Wu, Jing Liu, Yujia Shi, Yujia Sun, Fangtao Shao, Zhaoyang Wu, and Zhiwei Yang,  “Not only look, but also listen: Learning multimodal violence detection under weak supervision,”  in European Conference on Computer Vision. Springer, 2020, pp. 322–339.
  * [196] Romain Belmonte, Amel Aissaoui, Sofiane Mihoubi, Benjamin Allaert, José Mennesson, Ioan Marius Bilasco, and Laurent Goncalves,  “Barem: A multimodal dataset of individuals interacting with an e-service platform,”  in CBMI 2021-Content-based Multimedia Indexing, 2021.
  * [197] Iulia Lefter, Gertjan J Burghouts, and Léon JM Rothkrantz,  “Recognizing stress using semantics and modulation of speech and gestures,”  IEEE Transactions on Affective Computing, vol. 7, no. 2, pp. 162–175, 2015.
  * [198] Bruno M Peixoto, Bahram Lavi, Zanoni Dias, and Anderson Rocha,  “Harnessing high-level concepts, visual, and auditory features for violence detection in videos,”  Journal of Visual Communication and Image Representation, p. 103174, 2021.
  * [199] Xirong Li, Yujia Huo, Qin Jin, and Jieping Xu,  “Detecting violence in video using subclasses,”  in Proceedings of the 24th ACM international conference on Multimedia, 2016, pp. 586–590.
  * [200] Wen-Feng Pang, Qian-Hua He, Yong-jian Hu, and Yan-Xiong Li,  “Violence detection in videos based on fusing visual and audio information,”  in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2021, pp. 2260–2264.
  * [201] Peng Wu and Jing Liu,  “Learning causal temporal relation and feature discrimination for anomaly detection,”  IEEE Transactions on Image Processing, vol. 30, pp. 3513–3527, 2021\.