Panic Detection in Crowded Scenes
Abstract
A crowd is a gathering of a huge number of individuals in a confined area. Early identification and detection of unusual behaviors in terms of panic occurring in crowded scenes are very important. Panic detection comprises of formulating normal scene behaviors and detecting and identifying non-matching behaviors. However, panic detection and recognition is a very difficult problem, especially when considering diverse scenes. Many methods proposed to cope with these problems have limited robustness as the density of the crowd varies. In order to handle this challenge, this paper proposes the integration of different features into a unified model. Discriminant binary patterns and neighborhood information are used to model complex and unique motion patterns in order to characterize different levels of features for diverse types of crowd scenes, focusing in particular on the detection of panic and non-pedestrian entities. The proposed method was evaluated considering two benchmark datasets and outperformed five existing methods.
Downloads
References
L. Fei, B. Zhang, Y. Xu, D. Huang, W. Jia, J. Wen, “Local discriminant direction binary pattern for palmprint representation and recognition”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 30, No. 2, pp. 468-481, 2020 DOI: https://doi.org/10.1109/TCSVT.2019.2890835
W. Zhang, W. Zhang, K. Liu, J. Gu, “A feature descriptor based on local normalized difference for real-world texture classification”, IEEE Transactions on Multimedia, Vol. 20, No. 4, pp. 880-888, 2017 DOI: https://doi.org/10.1109/TMM.2017.2760102
J. T. Zhou, I. W. Tsang, S. S. Ho, K. R. Muller, “N-ary decomposition for multi-class classification”, Machine Learning, Vol. 108, No. 5, pp. 809-830, 2019 DOI: https://doi.org/10.1007/s10994-019-05786-2
Y. Hao, Z. J. Xu, Y. Liu, J. Wang, J. L. Fan, “Effective crowd anomaly detection through spatio-temporal texture analysis”, International Journal of Automation and Computing, Vol. 16, No. 1, pp. 27-39, 2019
Y. Liu, K. Hao, X. Tang, T. Wang, “Abnormal crowd behavior detection based on predictive neural network”, 2019 IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA), Dalian, China, October 17, 2019 DOI: https://doi.org/10.1109/ICAICA.2019.8873488
V. Mahadevan, W. Li, V. Bhalodia, N. Vasconcelos, “Anomaly detection in crowded scenes”, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Fransisco, USA, August 5, 2010 DOI: https://doi.org/10.1109/CVPR.2010.5539872
J. Ramos, N. Nedjah, L. de Macedo Mourelle, B. B. Gupta, “Visual data mining for crowd anomaly detection using artificial bacteria colony”, Multimedia Tools and Applications, Vol. 77, No. 14, pp. 17755-17777, 2018 DOI: https://doi.org/10.1007/s11042-017-5382-6
P. Ingole, V. Vyas, “Anomaly detection in crowd using optical flow and textural feature”, in: Soft Computing and Signal Processing, Advances in Intelligent Systems and Computing, Vol. 900, pp. 723-732, Springer, 2019 DOI: https://doi.org/10.1007/978-981-13-3600-3_69
S. D. Khan, M. Tayyab, M. K. Amin, A. Nour, A. Basalamah, S. Basalamah, S. A. Khan, “Towards a crowd analytic framework for crowd management in Majid-al-Haram”, 17th Scientific Meeting on Hajj & Umrah Research, 2017
K. Ahmad, N. Conci, F. G. De Natale, “A saliency-based approach to event recognition”, Signal Processing: Image Communication, Vol. 60, pp. 42-51, 2018 DOI: https://doi.org/10.1016/j.image.2017.09.009
H. Ullah, A. B. Altamimi, M. Uzair, M. Ullah, “Anomalous entities detection and localization in pedestrian flows”, Neurocomputing, Vol. 290, pp. 74-86, 2018 DOI: https://doi.org/10.1016/j.neucom.2018.02.045
R. Nawaratne, D. Alahakoon, D. De Silva, X. Yu, “Spatiotemporal anomaly detection using deep learning for real-time video surveillance”, IEEE Transactions on Industrial Informatics, Vol. 16, No. 1, pp. 393-402, 2019 DOI: https://doi.org/10.1109/TII.2019.2938527
K. Xu, T. Sun, X. Jiang, “Video anomaly detection and localization based on an adaptive intra-frame classification network”, IEEE Transactions on Multimedia, Vol. 22, No. 2, pp. 394-406, 2019 DOI: https://doi.org/10.1109/TMM.2019.2929931
J. Wan, A. Chan, “Adaptive density map generation for crowd counting”, IEEE International Conference on Computer Vision, Seoul, South Korea, October 27 – November 2, 2019 DOI: https://doi.org/10.1109/ICCV.2019.00122
X. Jiang, Z. Xiao, B. Zhang, X. Zhen, X. Cao, D. Doermann, L. Shao, “Crowd counting and density estimation by trellis encoder-decoder networks”, IEEE Conference on Computer Vision and Pattern Recognition, Long Beach, USA, June 15-20, 2019 DOI: https://doi.org/10.1109/CVPR.2019.00629
H. Yu, G. Pan, L. Zhang, Z. Li, M. Pan, “Translation domain segmentation model based on improved cosine similarity for crowd motion segmentation”, Journal of Electronic Imaging, Vol. 28, No. 2 2019 DOI: https://doi.org/10.1117/1.JEI.28.2.023011
N. Bisagno, B. Zhang, N. Conci, “Group LSTM: Group grajectory prediction in crowded scenarios”, in: Proceedings of the European conference on computer vision (ECCV), pp. 213-225, Springer, 2018 DOI: https://doi.org/10.1007/978-3-030-11015-4_18
R. Trabelsi, I. Jabri, F. Melgani, F. Smach, N. Conci, A. Bouallegue, “Complex-valued representation for RGB-D object recognition”, in: Pacific-Rim Symposium on Image and Video Technology, pp. 17-27. Springer, Cham, 2017 DOI: https://doi.org/10.1007/978-3-319-75786-5_2
L. Pan, H. Zhou, Y. Liu, M. Wang, “Global event influence model: integrating crowd motion and social psychology for global anomaly detection in dense crowds”, Journal of Electronic Imaging, Vol. 28, No. 2, 2019 DOI: https://doi.org/10.1117/1.JEI.28.2.023033
M. Xu, Z. Ge, X. Jiang, G. Cui, P. Lv, B. Zhou, C. Xu, “Depth information guided crowd counting for complex crowd scenes”, Pattern Recognition Letters, Vol. 125, pp. 563-569, 2019 DOI: https://doi.org/10.1016/j.patrec.2019.02.026
X. Alameda-Pineda, E. Ricci, N. Sebe, “Multimodal behavior analysis in the wild: an introduction”, in: Multimodal Behavior Analysis in the Wild, pp. 1-8. Academic Press, 2019 DOI: https://doi.org/10.1016/B978-0-12-814601-9.00011-0
M. Ullah, H. Ullah, N. Conci, F. G. B. De Natale, “Crowd behavior identification”, 2016 IEEE International Conference on Image Processing, Phoenix, USA, September 25-28, 2016 DOI: https://doi.org/10.1109/ICIP.2016.7532547
H. Kim, J. Han, S. Han, “Analysis of evacuation simulation considering crowd density and the effect of a fallen person”, Journal of Ambient Intelligence and Humanized Computing, Vol. 10, No. 12, pp. 4869-4879, 2019 DOI: https://doi.org/10.1007/s12652-019-01184-7
Y. Hao, Z. Xu, Y. Liu, J. Wang, J. Lun Fan, “Effective crowd anomaly detection through spatio-temporal texture analysis”, International Journal of Automation and Computing, Vol. 16, No. 1, pp. 27-39, 2019 DOI: https://doi.org/10.1007/s11633-018-1141-z
J. Li, L. Wei, F. Zhang, T. Yang, Z. Lu, “Joint deep and depth for object-level segmentation and stereo tracking in crowds”, IEEE Transactions on Multimedia, Vol. 21, No. 10, pp. 2531-2544, 2019 DOI: https://doi.org/10.1109/TMM.2019.2908350
K. Shimura, S. D. Khan, S. Bandini, K. Nishinari, “Simulation and evaluation of spiral movement of pedestrians: towards the tawaf simulator”, Journal of Cellular Automata, Vol. 11, No. 4, 2016
X. Zhang, X. Shu, Z. He, “Crowd panic state detection using entropy of the distribution of enthalpy”, Physica A: Statistical Mechanics and its Applications, Vol. 525, pp. 935-945, 2019 DOI: https://doi.org/10.1016/j.physa.2019.04.033
D. Kang, Z. Ma, A. B. Chan, “Beyond counting: comparisons of density maps for crowd analysis tasks—counting, detection, and tracking”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 29, No. 5, pp. 1408-1422, 2018 DOI: https://doi.org/10.1109/TCSVT.2018.2837153
M. Dimitrievski, P. Veelaert, W. Philips, “Behavioral pedestrian tracking using a camera and lidar sensors on a moving vehicle”, Sensors, Vol. 19, No. 2, 2019 DOI: https://doi.org/10.3390/s19020391
T. Figueiredo, R. Castro, “Passengers perceptions of airport branding strategies: the case of Tom Jobim International Airport–RIOgaleoo, Brazil”, Journal of Air Transport Management, Vol. 74, pp. 13-19, 2019 DOI: https://doi.org/10.1016/j.jairtraman.2018.09.010
Z. Zhang, K. Fu, X. Sun, W. Ren, “Multiple target tracking based on multiple hypotheses tracking and modified ensemble Kalman filter in multi-sensor fusion”, Sensors, Vol. 19, No. 14, 2019 DOI: https://doi.org/10.3390/s19143118
D. Ji, H. Lu, T. Zhang, “End to end multi-scale convolutional neural network for crowd counting”, in: Eleventh international conference on machine vision (ICMV 2018), Vol. 11041, International Society for Optics and Photonics, 2019 DOI: https://doi.org/10.1117/12.2522940
K. Singh, S. Rajora, D. K. Vishwakarma, G. Tripathi, S. Kumar, G. S. Walia, “Crowd anomaly detection using aggregation of ensembles of fine-tuned ConvNets”, Neurocomputing, Vol. 371, pp. 188-198, 2020 DOI: https://doi.org/10.1016/j.neucom.2019.08.059
S. K. Tan, N. Hu, W. Cai, “A data-driven path planning model for crowd capacity analysis”, Journal of Computational Science, Vol. 34, pp. 66-79, 2019 DOI: https://doi.org/10.1016/j.jocs.2019.05.003
S. D. Bansod, A. V. Nandedkar, “Crowd anomaly detection and localization using histogram of magnitude and momentum”, The Visual Computer, Vol. 36, pp. 609-620, 2020 DOI: https://doi.org/10.1007/s00371-019-01647-0
N. Conci, N. Bisagno, A. Cavallaro, “On modeling and analyzing crowds from videos”, in: Computer Vision for Assistive Healthcare, pp. 319-336, Academic Press, 2018 DOI: https://doi.org/10.1016/B978-0-12-813445-0.00011-3
M. S. Zitouni, A. Sluzek, H. Bhaskar, “Towards understanding socio-cognitive behaviors of crowds from visual surveillance data”, Multimedia Tools and Applications, 2019 DOI: https://doi.org/10.1007/s11042-019-08201-z
M. Marsden, K. Mc Guinness, S. Little, N. E. O’ Connor, “Holistic features for real-time crowd behaviour anomaly detection”, IEEE International Conference on Image Processing, Phoenix, USA, September 25-28, 2016 DOI: https://doi.org/10.1109/ICIP.2016.7532491
A. B. Chan, N. Vasconcelos, “Counting people with low-level features and bayesian regression”, IEEE Transactions on Image Processing, Vol. 21, No. 4, pp. 2160-2177, 2011 DOI: https://doi.org/10.1109/TIP.2011.2172800
M. D. Zeiler, G. W. Taylor, R. Fergus, “Adaptive deconvolutional networks for mid and high level feature learning”, 2011 International Conference on Computer Vision, Barcelona, Spain, November 6-13, 2011 DOI: https://doi.org/10.1109/ICCV.2011.6126474
L. Boominathan, S. S. S. Kruthiventi, R. V. Babu, “Crowdnet: a deep convolutional network for dense crowd counting”, in: Proceedings of the 24th ACM international conference on ,ultimedia, pp. 640-644, ACM, 2016 DOI: https://doi.org/10.1145/2964284.2967300
S. Yi, X. Wang, C. Lu, J. Jia, H. Li, “L0 regularized stationary-time estimation for crowd analysis”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 39, No. 5, pp. 981-994, 2017 DOI: https://doi.org/10.1109/TPAMI.2016.2560807
D. B. Sam, S. Surya, R. V. Babu, “Switching convolutional neural network for crowd counting”, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA, July 21-26, 2017 DOI: https://doi.org/10.1109/CVPR.2017.429
M. Marsden, K. Mc Guinness, S. Little, N. E. O’ Connor, “Resnetcrowd: a residual deep learning architecture for crowd counting, violent behaviour detection and crowd density level classification”, 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, Italy, August 29–September 1, 2017 DOI: https://doi.org/10.1109/AVSS.2017.8078482
V. A. Sandagi, V. M. Patel, “Generating high-quality crowd density maps using contextual pyramid CNNs”, 2017 IEEE International Conference on Computer Vision, Venice, Italy, October 22-29, 2017 DOI: https://doi.org/10.1109/ICCV.2017.206
L. B. Sallah, F. Fourati, “Systems modeling using deep Elman Neural Network”, Engineering, Technology & Applied Science Research, Vol. 9, No. 2, pp. 3881-3886, 2019 DOI: https://doi.org/10.48084/etasr.2455
S. R. Basha, J. K. Rani, “A comparative approach of dimensionality reduction techniques in text classification”, Engineering, Technology & Applied Science Research, Vol. 9, No. 6, pp. 4974-4979, 2019 DOI: https://doi.org/10.48084/etasr.3146
B. Alefs, G. Eschemann, H. Ramoser, C. Beleznai, “Road sign detection from edge orientation histograms”, 2007 IEEE Intelligent Vehicles Symposium, Istanbul, Turkey, June 13-15, 2007 DOI: https://doi.org/10.1109/IVS.2007.4290246
J. Clemons, “SIFT: scale invariant feature transform”, available at: https://pdfs.semanticscholar.org/19d1/c9a4546d840269ef534f6c1c8e3798ce81ac.pdf
P. H. Gosselin, N. Murray, H. Jegou, F. Perronnin, “Revisiting the fisher vector for fine-grained classification”, Pattern Recognition Letters, Vol. 49, pp. 92-98, 2014 DOI: https://doi.org/10.1016/j.patrec.2014.06.011
S. Dasgupta, “Experiments with random projection”, in: Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, pp. 143-151, ACM, 2013
G. V. de Lima, P. T. Saito, F. M. Lopes, P. H. Bugatti, “Classification of texture based on bag-of-visual-words through complex networks”, Expert Systems with Applications, Vol. 133, pp. 215-224, 2019 DOI: https://doi.org/10.1016/j.eswa.2019.05.021
Statistical Visual Computing Lab–UC San Diego, “UCSD anomaly detection dataset", available at: http://www.svcl.ucsd.edu/projects/anomaly/dataset.htm
University of Minnesota, “Monitoring Human Activity”, available at: http://mha.cs.umn.edu/
Downloads
How to Cite
License
Authors who publish with this journal agree to the following terms:
- Authors retain the copyright and grant the journal the right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) after its publication in ETASR with an acknowledgement of its initial publication in this journal.
