›› 2017, Vol. 32 ›› Issue (3): 470-479.doi: 10.1007/s11390-017-1737-8

Special Issue: Artificial Intelligence and Pattern Recognition; Computer Graphics and Multimedia

• Special Section of CVM 2017 • Previous Articles     Next Articles

Collective Representation for Abnormal Event Detection

Renzhen Ye1,2, Xuelong Li1, Fellow, IEEE   

  1. 1. Center for Optical Imagery Analysis and Learning(OPTIMAL), State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an 710119, China;
    2. School of Electronics and Information Engineering, Xi'an Jiaotong University, Xi'an 710119, China
  • Received:2016-12-26 Revised:2017-03-21 Online:2017-05-05 Published:2017-05-05
  • Contact: 10.1007/s11390-017-1737-8
  • About author:Renzhen Ye is currently an associate professor with the Department of Mathematics, Huazhong Agricultural University, Wuhan. She is pursuing her Ph.D. degree in the Center for OPTical IMagery Analysis and Learning, State Key Laboratory of Transient Optics and Photonics, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an. Her research interests include partial differential equations, mathematical mechanization and mathematical physics, and machine learning.
  • Supported by:

    This work is supported by the Defense Basic Research Program under Grant No. BA1320110042, and the Open Research Fund of the Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences under Grant No. LSIT201408.

Abnormal event detection in crowded scenes is a hot topic in computer vision and information retrieval community. In this paper, we study the problems of detecting anomalous behaviors within the video, and propose a robust collective representation with multi-feature descriptors for abnormal event detection. The proposed method represents different features in an identical representation, in which different features of the same topic will show more common properties. Then, we build the intrinsic relation between different feature descriptors and capture concept drift in the video sequence, which can robustly discriminate between abnormal events and normal events. Experimental results on two benchmark datasets and the comparison with the state-of-the-art methods validate the effectiveness of our method.

[1] Cong Y, Yuan J, Liu J. Sparse reconstruction cost for abnormal event detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.3449- 3456.

[2] Zhao B, Li F F, Xing E P. Online detection of unusual events in videos via dynamic sparse coding. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.3313-3320.

[3] Zhou Y, Bai X, Liu W et al. Swarm fusion for visual tracking. International Journal of Computer Vision, 2016, 118(3): 337-363.

[4] Li C, Han Z, Ye Q, Jiao J. Abnormal behavior detection via sparse reconstruction analysis of trajectory. In Proc. the 6th International Conference on Image and Graphics, August 2011, pp.807-810.

[5] Piciarelli C, Micheloni C, Foresti G L. Trajectory-based anomalous event detection. IEEE Transactions on Circuits and Systems for Video Technology, 2008, 18(11): 1544- 1554.

[6] Lu X, Wang Y, Yuan Y. Alternatively constrained dictionary learning for image superresolution. IEEE Transactions on Cybernetics, 2014, 44(3): 366-377.

[7] Mehran R, Oyama A, Shah M. Abnormal crowd behavior detection using social force model. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp.935-942.

[8] Lu X, Yuan Y, Zheng X. Jointly dictionary learning for change detection in multispectral imagery. IEEE Transactions on Cybernetics, 2017, 47(4): 884-897.

[9] Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM Computing Surveys, 2009, 41(3): 15:1-15:58.

[10] Vishwakarma S, Agrawal A. A survey on activity recognition and behavior understanding in video surveillance. The Visual Computer, 2013, 29(10): 983-1009.

[11] Borges P V K, Conci N, Cavallaro A. Video-based human behavior understanding: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 2013, 23(11): 1993-2008.

[12] Bruckstein A, Donoho D, Elad M. From sparse solutions of systems of equations to sparse modeling of signals and images. SIAM Rev., 2009, 51(1): 34-81.

[13] Lu X, Wu H, Yuan Y. Double constrained NMF for hyperspectral unmixing. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(5): 2746-2758.

[14] Lu X, Wang Y, Yuan Y. Graph regularized low-rank representation for destriping of hyperspectral images. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(7-1): 4009-4018.

[15] Song B, Li J, Mura M D, Li P, Plaza A, Bioucas-Dias J M, Benediktsson J A, Chanussot J. Remotely sensed image classification using sparse representations of morphological attribute profiles. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(8): 5122-5136.

[16] Lu C, Shi J, Jia J. Abnormal event detection at 150 FPS in MATLAB. In Proc. IEEE International Conference on Computer Vision, December 2013, pp.2720-2727.

[17] Mo X, Monga V, Bala R, Fan Z. Adaptive sparse representations for video anomaly detection. IEEE Transactions on Circuits and Systems for Video Technology, 2014, 24(4): 631-645.

[18] Basharat A, Gritai A, Shah M. Learning object motion patterns for anomaly detection and improved object detection. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2008.

[19] Yuan Y, Fang J, Wang Q. Online anomaly detection in crowd scenes via structure analysis. IEEE Transactions on Cybernetics, 2015, 45(3): 562-575.

[20] Itti L, Baldi P. A principled approach to detecting surprising events in video. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2005, pp.631-637.

[21] Han J, Zhang D, Hu X, Guo L, Ren J, Wu F. Background prior-based salient object detection via deep reconstruction residual. IEEE Trans. Circuits and Systems for Video Technology, 2015, 25(8): 1309-1321.

[22] Han J, Zhang D, Wen S, Guo L, Liu T, Li X. Two-stage learning to predict human eye fixations via SDAEs. IEEE Trans. Cybernetics, 2016, 46(2): 487-498.

[23] Qi W, Cheng M, Borji A, Lu H, Bai L. SaliencyRank: Twostage manifold ranking for salient object detection. Computational Visual Media, 2016, 1(4): 309-320.

[24] Cheng M, Mitra N J, Huang X, Torr P H S, Hu S. Global contrast based salient region detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582.

[25] Boiman O, Irani M. Detecting irregularities in images and in video. International Journal of Computer Vision, 2007, 74(1): 17-31.

[26] Kratz L, Nishino K. Anomaly detection in extremely crowded scenes using spatio-temporal motion pattern models. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp.1446-1453.

[27] Wu S, Moore B, Shah M. Chaotic invariants of Lagrangian particle trajectories for anomaly detection in crowded scenes. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2010, pp.2054-2060.

[28] Cheng H Y, Hwang J N. Integrated video object tracking with applications in trajectory-based event detection. Journal of Visual Communication and Image Representation, 2011, 22(7): 673-685.

[29] Cui X, Liu Q, Gao M, Metaxas D N. Abnormal detection using interaction energy potentials. In Proc. the 24th IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.3161-3167.

[30] Saligrama V, Chen Z. Video anomaly detection based on local statistical aggregates. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2012, pp.2112-2119.

[31] Popoola O P, Wang K. Video-based abnormal human behavior recognition - A review. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(6): 865-878.

[32] Sodemann A A, Ross M P, Borghetti B J. A review of anomaly detection in automated surveillance. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 2012, 42(6): 1257-1272.

[33] Li T, Chang H, Wang M, Ni B, Hong R, Yan S. Crowded scene analysis: A survey. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(3): 367-386.

[34] Zhong H, Shi J, Visontai M. Detecting unusual activity in video. In Proc. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2, June 27-July 2, 2004, pp.819-826.

[35] Benezeth Y, Jodoin P M, Saligrama V, Rosenberger C. Abnormal events detection based on spatio-temporal cooccurences. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp.2458-2465.

[36] del Rincon J, Lewandowski M, Nebel J C, Makris D. Generalized Laplacian eigenmaps for modeling and tracking human motions. IEEE Transactions on Cybernetics, 2014, 44(9): 1646-1660.

[37] Azhar F, Tjahjadi T. Significant body point labeling and tracking. IEEE Transactions on Cybernetics, 2014, 44(9): 1673-1685.

[38] Xie Y, Zhang W, Li C, Lin S, Qu Y, Zhang Y. Discriminative object tracking via sparse representation and online dictionary learning. IEEE Transactions on Cybernetics, 2014, 44(4): 539-553.

[39] Yang Y, Hu W, Xie Y, Zhang W, Zhang T. Temporal restricted visual tracking via reverse-low-rank sparse learning. IEEE Transactions on Cybernetics, 2016, 47(2): 485-498.

[40] Zhang Y, Chen X, Lin L, Xia C, Zou D. High-level representation sketch for video event retrieval. Science in China Series F: Information Sciences, 2016, 59(7): 072103.

[41] Adam A, Rivlin E, Shimshoni I, Reinitz D. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2008, 30(3): 555-560.

[42] Kim J, Grauman K. Observe locally, infer globally: A spacetime MRF for detecting abnormal activities with incremental updates. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp.2921-2928.

[43] Mahadevan V, Li W, Bhalodia V, Vasconcelos N. Anomaly detection in crowded scenes. In Proc. the 23rd IEEE Conference on Computer Vision and Pattern Recognition, June 2010, pp.1975-1981.

[44] Li W, Mahadevan V, Vasconcelos N. Anomaly detection and localization in crowded scenes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(1): 18-32.

[45] Cong Y, Yuan J, Liu J. Abnormal event detection in crowded scenes using sparse representation. Pattern Recognition, 2013, 46(7): 1851-1864.

[46] Thida M, Eng H L, Remagnino P. Laplacian eigenmap with temporal constraints for local abnormality detection in crowded scenes. IEEE Transactions on Cybernetics, 2013, 43(6): 2147-2156.

[47] Kaltsa V, Briassouli A, Kompatsiaris I, Hadjileontiadis L J, Strintzis M G. Swarm intelligence for detecting interesting events in crowded environments. IEEE Transactions on Image Processing, 2015, 24(7): 2153-2166.

[48] Reddy V, Sanderson C, Lovell B C. Improved anomaly detection in crowded scenes via CellBased analysis of foreground speed, size and texture. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, June 2011, pp.55-61.

[49] Censor Y, Zenios S. Parallel optimization: Theory, algorithms and applications. Oxford University Press, 1997.
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[3] Min Youli; Min Yinghua;. A Fault-Tolerant and Heuristic Routing Algorithm for Faulty Hypercubes[J]. , 1995, 10(6): 536 -544 .
[4] Liu Tieying; Ye Xinming;. An Algorithm for Determining Minimal Reduced-Coverings of Acyclic Database Schemes[J]. , 1996, 11(4): 347 -355 .
[5] Wang Jian;. Integration Model of Eye-Gaze, Voice and Manual Response in Multimodal User Interface[J]. , 1996, 11(5): 512 -518 .
[6] Zong Chengqing; Chen Zhaoxiong; Huang Heyan;. Parsing with Dynamic Rule Selection[J]. , 1997, 12(1): 90 -96 .
[7] Lu Weifeng; Zhang Yuping;. Experimental Study on Strategy of CombiningSAT Algorithms[J]. , 1998, 13(6): 608 -614 .
[8] XI Haifeng; LUO Yupin; YANG Shiyuan;. An Approach to Active Learning for Classifier Systems[J]. , 1999, 14(4): 372 -378 .
[9] ZHANG Wensong; JIN Shiyao; WU Quanyuan;. LinuxDirector: A Connection Director for Scalable Internet Services[J]. , 2000, 15(6): 560 -571 .
[10] Hao-Jun Ai, Shui-Xian Chen, and Rui-Min Hu. Introduction to AVS Audio[J]. , 2006, 21(3): 360 -365 .

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