article

Abnormal events' detection in crowded scenes

Authors:

Mourad ZaiedAuthors Info & Claims

Multimedia Tools and Applications, Volume 77, Issue 19

Pages 24843 - 24864

https://doi.org/10.1007/s11042-018-5701-6

Published: 01 October 2018 Publication History

Abstract

In this paper, two new methods are developed in order to detect and track unexpected events in scenes. The process of detecting people may face some difficulties due to poor contrast, noise and the small size of the defects. For this purpose,the perfect knowledge of the geometry of these defects is an essential step in assessing the quality of detection. First, we collected statistical models of the element for each individual for time tracking of different people using the technique of Gaussian mixture model (GMM). Then we improved this method to detect and track the crowd(IGMM). Thereafter, we adopted two methods: the differential method of Lucas and Kanade(LK) and the method of optical flow estimation of Horn Schunck(HS) for optical flow representation. Then, we proposed a novel descriptor, named the Distribution of Magnitude of Optical Flow (DMOF) for anomalous events' detection in the surveillance video. This descriptor represents an algorithm whose aim is to accelerate the action of abnormal events' detection based on a local adjustment of the velocity field by manipulating the light intensity.

References

[1]

Adam A, Rivlin E, Shimshoni I, Reinitz D (2008) Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans Pattern Anal Mach Intell 30:555---560

Digital Library

[2]

Benezeth Y, Jodoin PM, Saligrama V, Rosenberger C (2009) Abnormal events detection based on spatio-temporal co-occurences. In: IEEE conference on computer vision and pattern recognition, CVPR, pp 2458---2465

[3]

Benezeth Y, Jodoin P-M, Saligrama V (2011) Abnormality detection using low-level co-occurring events. Pattern Recognit Lett 32(3):423431

Digital Library

[4]

Biswas S, Gupta V (2017) Abnormality detection in crowd videos by tracking sparse components. Mach Vis Appl 28(1-2):35---48

Digital Library

[5]

Boiman O, Irani M (2007) Detecting irregularities in images and in video. Int J Comput Vis 74(1):17---31

Digital Library

[6]

Cao T, Wu X, Guo J, Yu S, Xu Y (2009) Abnormal crowd motion analysis. In: IEEE international conference on robotics and biomimetics (ROBIO) 2009, IEEE, Guilin, pp 1709---1714

Digital Library

[7]

Chaturvedi PP, Rajput AS, Jain A (2013) Video object tracking based on automatic background segmentation and updating using RBF neural network. International Journal of Advanced Computer Research 3:866

[8]

Colque RVHM, Caetano C, De Andrade MTL et al (2017) Histograms of optical flow orientation and magnitude and entropy to detect anomalous events in videos. IEEE Trans Circuits Syst Video Technol 27(3):673---682

Digital Library

[9]

Cong Y, Yuan J, Liu J (2011) Sparse reconstruction cost for abnormal event detection. In: Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on. IEEE, 2011. pp 3449---3456

Digital Library

[10]

Direkoglu C, Sah M, O'Connor NE (2017) Abnormal crowd behavior detection using novel optical flow-based features. In: 14th IEEE international conference on advanced video and signal based surveillance (AVSS), 2017. IEEE

[11]

Direkoglu C, Sah M, O'Connor NE (2017) Abnormal crowd behavior detection using novel optical flow-based features. In: Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference on. IEEE, 2017. pp 1---6

[12]

Drews P, Quintas J, Dias J et al (2010) Crowd behavior analysis under cameras network fusion using probabilistic methods. In: 13th conference on information fusion (FUSION), p 18

[13]

Fang Z, Fei F, Fang Y et al (2016) Abnormal event detection in crowded scenes based on deep learning. Multimedia Tools and Applications 75(22):14617---14639

Digital Library

[14]

Hauhan AK, Krishan P (2013) Moving object tracking using gaussian mixture model and optical flow. International Journal of Advanced Research in Computer Science and Software Engineering 3(4)

[15]

Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17 (1---3):185---203

Digital Library

[16]

Hu M, Ali S, Shah M (2008) Detecting global motion patterns in complex videos. In: 19th international conference on pattern recognition. ICPR 2008. IEEE, pp 1---5

[17]

Hu Y, Zhang Y, Davis L (2013) Unsupervised abnormal crowd activity detection using semiparametric scan statistic. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 767---774

Digital Library

[18]

Junior J, Mussef S, Jung C (2010) Crowd analysis using computer vision techniques. IEEE Signal Process 27:66---77

[19]

Kaviani R, Ahmadi P, Gholampour I (2014) Incorporating fully sparse topic models for abnormality detection in traffic videos. In: Proceeding of the international econference on computer and knowledge engineering, Mashhad, Iran, pp 586---591

[20]

Khatrouch M, Gnouma M, Ejbali R, Zaied M (2017) Deep learning architecture for recognition of abnormal activities. In: The 10th international conference on machine vision, Vienna, Austria

[21]

Kim J, Grauman K (2009) Observe locally, infer globally: a space-time MRF for detecting abnormal activities with incremental updates. In: Proceeding of the IEEE conference on computer vision and pattern recognition, pp 2921---2928

[22]

Kratz L, Nishino K (2010) Tracking with local spatio-temporal motion patterns in extremely crowded scenes. In: IEEE Conference on computer vision and pattern recognition (CVPR), 2010. IEEE, pp 693---700

[23]

Kratz L, Nishino K (2012) Tracking pedestrians using local spatio-temporal motion patterns in extremely crowded scenes. IEEE Trans Pattern Anal Mach Intell 34 (5):9871002

Digital Library

[24]

Li A, Miao Z, Cen Y et al (2017) Anomaly detection using sparse reconstruction in crowded scenes. Multimedia Tools and Applications 76(24):26249---26271

Digital Library

[25]

Li J, Hospedales TM, Gong S, Xiang T (2010) Learning rare behaviours. In: Proceeding of the Asian conference on computer vision, Queenstown, New Zealand, pp 293---307

Digital Library

[26]

Li W, Mahadevan V, Vasconcelos N (2014) Anomaly detection and localization in crowded scenes. IEEE Trans Pattern Anal Mach Intell 36(1):18---32

Digital Library

[27]

Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: IEEE international conference on computer vision (ICCV), 2013, IEEE 27202727

Digital Library

[28]

Lu C, Shi J, Jia J (2013) Abnormal event detection at 150 fps in matlab. In: Proceedings of the IEEE international conference on computer vision, pp 2720---2727

Digital Library

[29]

Lui X, Rittscher J, Perera A, Krahnstoever N (2005) Detecting and counting people in surveillance applications. In: Advanced video and signal based surveillance, pp 306---311

[30]

Mahadevan V, Li W, Bhalodia V, Masconcelos V (2010) Anomaly detection in crowded scenes. In: Proceeding of the IEEE conference on computer vision and pattern recognition

[31]

Mariem G, Ridha E, Mourad Z (2016) Detection of abnormal movements of a crowd in a video scene. International Journal of Computer Theory and Engineering 8 (5):398

[32]

Marques JS, Jorge PM, Abrantes AJ, Lemos JM (2003) Tracking groups of pedestrians in video sequences. In: IEEE computer society conference on computer vision and pattern recognition workshops, vol 9, p 101101

[33]

Marzat J (2008) INRIA - Estimation temps réel du flot optique, ISA

[34]

Mehran R, Oyama A, Shah M (2009) Abnormal crowd behavior detection using social force model. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 935---942

[35]

Mousse AM (2016) Reconnaissance dactivits humaines partir de squences multi-camras: application la detection de chute de personne. Universit du Littoral-Cted'Opale

[36]

Nam Y (2014) Crowd flux analysis and abnormal event detection in unstructured and structured scenes. Multimedia Tools and Applications 72(3):3001---3029

Digital Library

[37]

Pérez-Rúa J-M, Basset A, Bouthemy P (2017) Detection and localization of anomalous motion in video sequences from local histograms of labeled affine flows. Frontiers in ICT 4:10

[38]

PETS Dataset. http://www.cvg.reading.ac.uk/PETS2009/a.html

[39]

Rao AS, Gubbi J, Rajasegarar S, Marusic S, Palaniswami M (2014) Detection Of anomalous crowd behaviour using hyperspherical clustering. In: International conference on digital lmage computing: techniques and applications (DlCTA). IEEE, pp 1---8

[40]

Rodrigues de Almeida I, Jung CR (2013) Change detection in human crowds. In: Proceeding of the conference on graphics, patterns and images, pp 63---69

Digital Library

[41]

Roshtkhari MJ, Levine MD (2013) An on-line, real-time learning method for detecting anomalies in videos using spatio-temporal compositions. Comput Vis Image Underst 117(10):1436---1452

Digital Library

[42]

Ryan D, Denman S, Fookes C, Clinton B, Sridharan S (2011) Textures of optical flow for real-time anomaly detection in crowds. In: 2011 8th IEEE international conference on advanced video and signal based surveillance (AVSS), pp 230---235.

Digital Library

[43]

Santosh DHH, Venkatesh P, Poornesh P, Rao LN, Kumar NA (2013) Tracking multiple moving objects using gaussian mixture model. International Journal of Soft Computing and Engineering (IJSCE) 3(2):114---119

[44]

Shah AJ (2016) Abnormal behavior detection using tensor factorization. (Doctoral Dissertation, Cole de Technologie Suprieure)

[45]

Shi Y, Liu Y, Zhang Q, Yi Y, Li W (2016) Saliency-based abnormal event detection in crowded scenes. J Electron Imaging 25(6)

[46]

Tu P, Sebastian T, Doretto G, Krahnstoever N, Rittscher J, Yu T (2008) Unified crowd segmentation. In: European conference on computer vision, vol 5305, pp 691---704

[47]

Tziakos I, Cavallaro A, Xu LQ (2010) Local abnormality detection in video using subspace learning. In: Seventh IEEE international conference on advanced video and signal based surveillance (AVSS), 2010, pp 519---525

Digital Library

[48]

University of Reading, PETS 2009 Dataset S3 Rapid Dispersion, available from http://www.cvg.reading.ac.uk/PETS2009/a.html

[49]

Unusual crowd activity dataset of University of Minnesota, from http://mha.cs.umn.edu/movies/crowdactivity-all.avi

[50]

Varadarajan J, Odobez J-M (2009) Topic models for scene analysis and abnormality detection. In: Proceeding of the international conference on computer vision workshops, Kyoto, Japan, pp 1338--- 1345

[51]

Wang J, Xu Z (2016) Spatio-temporal texture modelling for real-time crowd anomaly detection. Comput Vis Image Underst 144:177---187

Digital Library

[52]

Wang T, Snoussi H (2014) Detection of abnormal visual events via global optical flow orientation histogram. IEEE Trans Inf Forensics Secur 9(6):988---998

Digital Library

[53]

Wu S, Moore BE, Shah M (2010) Chaotic invariants of lagrangian particle trajectories for anomaly detection in crowded scenes. In: IEEE conference on computer vision and pattern recognition, pp 2054---2060

[54]

Wu S, Wong HS, Yu Z (2014) A bayesian model for crowd escape behavior detection. IEEE Trans Circuits Syst Video Technol 24(1):85---98

Digital Library

[55]

Xu D, Song R, Wu X, Li N, Feng W, Qian H (2014) Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts. Neurocomputing 143:144---152

Digital Library

[56]

Zhan B, Monekosso DN, Remagnino P, Velastin SA, Il-Qun X (2008) Crowd analysis: a survey. Mach Vis Appl 19(5---6):345---357

Digital Library

[57]

Zhang T, Yang Z, Jia W et al (2016) A new method for violence detection in surveillance scenes. Multimedia Tools and Applications 75(12):7327---7349

Digital Library

[58]

Zhang Y, Qin L, Yao H et al (2012) Abnormal crowd behavior detection based on social attribute-aware force model. In: 19th IEEE international conference on image processing (ICIP), 2012. IEEE, pp 2689---2692

Cited By

Mukesh Krishnan MThanga Ramya SRamar K(2024)Detecting multiple moving objects for unusual crowd activities detection in video surveillance systemJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23383346:2(3789-3798)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-233833
Ullah FObaidat MUllah AMuhammad KHijji MBaik S(2023)A Comprehensive Review on Vision-Based Violence Detection in Surveillance VideosACM Computing Surveys10.1145/356197155:10(1-44)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3561971
Singh JKumar M(2023)Conditional autoregressive-tunicate swarm algorithm based generative adversarial network for violent crowd behavior recognitionArtificial Intelligence Review10.1007/s10462-023-10571-856:Suppl 2(2099-2123)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10462-023-10571-8
Show More Cited By

Abnormal events' detection in crowded scenes
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision tasks
  2. Computer graphics
    1. Animation

Recommendations

Density aware anomaly detection in crowded scenes

Coherent nature of crowd movement allows representing the crowd motion using sparse features. However, surveillance videos recorded at different periods of time are likely to have different crowd densities and motion characteristics. These varying scene ...
Deep Representation for Abnormal Event Detection in Crowded Scenes
MM '16: Proceedings of the 24th ACM international conference on Multimedia

Abnormal event detection is extremely important, especially for video surveillance. Nowadays, many detectors have been proposed based on hand-crafted features. However, it remains challenging to effectively distinguish abnormal events from normal ones. ...
Tracking Using Motion Patterns for Very Crowded Scenes
Proceedings, Part II, of the 12th European Conference on Computer Vision --- ECCV 2012 - Volume 7573

This paper proposes Motion Structure Tracker MST to solve the problem of tracking in very crowded structured scenes. It combines visual tracking, motion pattern learning and multi-target tracking. Tracking in crowded scenes is very challenging due to ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Multimedia Tools and Applications

Multimedia Tools and Applications Volume 77, Issue 19

October 2018

1576 pages

ISSN:1380-7501

Issue’s Table of Contents

Copyright © Copyright © 2018 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Kluwer Academic Publishers

United States

Publication History

Published: 01 October 2018

Author Tags

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

10
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 20 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Mukesh Krishnan MThanga Ramya SRamar K(2024)Detecting multiple moving objects for unusual crowd activities detection in video surveillance systemJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23383346:2(3789-3798)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-233833
Ullah FObaidat MUllah AMuhammad KHijji MBaik S(2023)A Comprehensive Review on Vision-Based Violence Detection in Surveillance VideosACM Computing Surveys10.1145/356197155:10(1-44)Online publication date: 2-Feb-2023
https://dl.acm.org/doi/10.1145/3561971
Singh JKumar M(2023)Conditional autoregressive-tunicate swarm algorithm based generative adversarial network for violent crowd behavior recognitionArtificial Intelligence Review10.1007/s10462-023-10571-856:Suppl 2(2099-2123)Online publication date: 1-Nov-2023
https://dl.acm.org/doi/10.1007/s10462-023-10571-8
Nguyen TNguyen TBouchara F(2022)Dynamic texture description using adapted bipolar-invariant and blurred featuresMultidimensional Systems and Signal Processing10.1007/s11045-022-00826-y33:3(945-979)Online publication date: 1-Sep-2022
https://dl.acm.org/doi/10.1007/s11045-022-00826-y
Alhothali ABalabid AAlharthi RAlzahrani BAlotaibi RBarnawi A(2022)Anomalous event detection and localization in dense crowd scenesMultimedia Tools and Applications10.1007/s11042-022-13967-w82:10(15673-15694)Online publication date: 15-Oct-2022
https://dl.acm.org/doi/10.1007/s11042-022-13967-w
Liu ZXia H(2021)Video Abnormal Behavior Detection Based on Optical Flow Method and Convolutional Neural NetworkProceedings of the 2021 International Conference on Human-Machine Interaction10.1145/3478472.3478476(13-17)Online publication date: 7-May-2021
https://dl.acm.org/doi/10.1145/3478472.3478476
Ilyas ZAziz ZQasim TBhatti NHayat M(2021)A hybrid deep network based approach for crowd anomaly detectionMultimedia Tools and Applications10.1007/s11042-021-10785-480:16(24053-24067)Online publication date: 1-Jul-2021
https://dl.acm.org/doi/10.1007/s11042-021-10785-4
Hatirnaz ESah MDirekoglu C(2020)A novel framework and concept-based semantic search Interface for abnormal crowd behaviour analysis in surveillance videosMultimedia Tools and Applications10.1007/s11042-020-08659-279:25-26(17579-17617)Online publication date: 1-Jul-2020
https://dl.acm.org/doi/10.1007/s11042-020-08659-2
Gnouma MLadjailia AEjbali RZaied M(2019)Stacked sparse autoencoder and history of binary motion image for human activity recognitionMultimedia Tools and Applications10.1007/s11042-018-6273-178:2(2157-2179)Online publication date: 1-Jan-2019
https://dl.acm.org/doi/10.1007/s11042-018-6273-1
Chen XLi Q(2019)Event modeling and mining: a long journey toward explainable eventsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-019-00545-029:1(459-482)Online publication date: 1-Jul-2019
https://dl.acm.org/doi/10.1007/s00778-019-00545-0

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents