Volume 15, Issue 3 (12-2018)                   JSDP 2018, 15(3): 75-88 | Back to browse issues page

‎ 10.29252/jsdp.15.3.75

XML Persian Abstract Print

Application of Sparse Representation and Camera Collaboration in Visual Surveillance Systems
Asghar Feizi *
Damghan University
Abstract:   (3541 Views)

With the growth of demand for security and safety, video-based surveillance systems have been employed in a large number of rural and urban areas. The problem of such systems lies in the detection of patterns of behaviors in a dataset that do not conform to normal behaviors. Recently, for behavior classification and abnormal behavior detection, the sparse representation approach is used. In this paper, feature sparse representation in a multi-view network is used for the purpose of behavior classification and abnormal behavior detection. To serve this purpose, a geometrically independent feature is first extracted for each location in the image. Then, for each camera view, the matrix for the dictionary A is calculated, which is considered as a set of behavior models. In order to share information and make use of the trained models, the learned dictionary matrix from the experienced camera is transferred to inexperienced cameras. The transferred matrix in the new camera is subsequently used to detect abnormal behaviors. A hierarchical method on the basis of spectral clustering is proposed for learning the dictionary matrix. After sparse feature representation, a measurement criterion, which makes use of the representation, is presented for abnormal behavior detection. The merit of the method proposed in this paper is that the method does not require correspondence across cameras. The direct use of the dictionary matrix and transfer of the learned dictionary matrix from the experienced camera to inexperienced ones, are tested on several real-world video datasets. In both cases, desirable improvements in abnormal behavior detection are obtained. The experimental results point to the efficacy of the proposed method for camera cooperation in order to detect abnormal behaviors.
 

Keywords: Visual Surveillance, Sparse Representation, Behavior Recognition, Abnormal Behavior, Camera Collaboration
Full-Text [PDF 5588 kb]   (1737 Downloads)    
Type of Study: Research | Subject: Paper
Received: 2017/09/28 | Accepted: 2018/08/6 | Published: 2018/12/19 | ePublished: 2018/12/19
References
1. [1] C. Chi-Hung, H. Jun-Wei, T. Luo-Wei, C. Sin-Yu, and F. Kuo-Chin, "Carried object detection using ratio histogram and its application to suspicious event analysis," IEEE Trans. Circuits Syst. Video Technol., vol. 19, no. 6, pp. 911–916, 2009. [DOI:10.1109/TCSVT.2009.2017415]
2. [2] T. Xiang and S. Gong, "Video behaviour profiling for anomaly detection," IEEE Trans. Pattern Anal. Mach. Intell., vol. 30, no. 5, pp. 893–908, 2008. [DOI:10.1109/TPAMI.2007.70731] [PMID]
3. [3] D. Xu, R. Song, X. Wu, N. Li, W. Feng, and H. Qian, "Video anomaly detection based on a hierarchical activity discovery within spatio-temporal contexts," Neurocomputing, vol. 143, pp. 144–152, 2014. [DOI:10.1016/j.neucom.2014.06.011]
4. [4] M. Sabokrou, M. Fayyaz, M. Fathy, Z. Moayed, and R. Klette, "Deep-anomaly: Fully convolutional neural network for fast anomaly detection in crowded scenes," Computer Vision and Image Understanding, no. February, Elsevier, pp. 0–1, 2018.
5. [5] Y. Yuan, D. Wang, and Q. Wang, "Anomaly Dete-ction in Traffic Scenes via Spatial-Aware Motion Reconstruction," IEEE Trans. Intell. Transp. Syst., vol. 18, no. 5, pp. 1198–1209, 2017. [DOI:10.1109/TITS.2016.2601655]
6. [6] X. Gu, J. Cui, and Q. Zhu, "Abnormal crowd behavior detection by using the particle entropy," Optik (Stuttg)., vol. 125, no. 14, pp. 3428–3433, 2014. [DOI:10.1016/j.ijleo.2014.01.041]
7. [7] Y. Zhang, H. Lu, L. Zhang, and X. Ruan, "Combin-ing motion and appearance cues for anomaly detection," Pattern Recognit., vol. 51, pp. 443–452, 2016. [DOI:10.1016/j.patcog.2015.09.005]
8. [8] B. Antić and B. Ommer, "Video parsing for ab-normality detection," Proc. IEEE Int. Conf. Com-put. Vis., pp. 2415–2422, 2011.
9. [9] S. Zhu, J. Hu, and Z. Shi, "Local abnormal behav-ior detection based on optical flow and spatio-temporal gradient," Multimed. Tools Appl., vol. 75, no. 15, pp. 9445–9459, 2016. [DOI:10.1007/s11042-015-3122-3]
10. [10] J. Kim and K. Grauman, "Observe locally, infer globally: a space–time mrf for detecting ab-normal activities with incremental updates," in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition, 2009, no. June.
11. [11] A. L. Hou, J. L. Guo, C. J. Wang, L. Wu, and F. Li, "Abnormal behavior recognition based on trajectory feature and regional optical flow," in Proceedings - 2013 7th International Conference on Image and Graphics, ICIG 2013, 2013, pp. 643–649. [DOI:10.1109/ICIG.2013.134]
12. [12] V. Saligrama, J. Konrad, and P. M. Jodoin, "Vi-deo anomaly identification," IEEE Signal Pro-cess. Mag., vol. 27, no. 5, pp. 18–33, 2010. [DOI:10.1109/MSP.2010.937393]
13. [13] J. A. Rodríguez-Serrano and S. Singh, "Trajec-tory clustering in CCTV traffic videos using probability product kernels with hidden Markov models," Pattern Anal. Appl., vol. 15, no. 4, pp. 415–426, 2012. [DOI:10.1007/s10044-012-0269-7]
14. [14] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Anomaly detection and localization in crowded scenes using connected component analysis," 2017.
15. [15] X. Wang, X. Ma, and E. Grimson, "Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Mod-els.pdf," vol. 31, no. 3, pp. 1–35, 2007.
16. [16] S. Li, C. Liu, and Y. Yang, "Anomaly Detection Based on Maximum a Posteriori," Pattern Reco-gnit. Lett., vol. 0, pp. 1–7, 2017. [DOI:10.1016/j.patcog.2016.09.013]
17. [17] C. Simon, J. Meessen, and C. De Vleeschouwer, "Visual event recognition using decision trees," Multimed. Tools Appl., vol. 50, no. 1, pp. 95–121, 2010. [DOI:10.1007/s11042-009-0364-y]
18. [18] S. Amraee, A. Vafaei, K. Jamshidi, and P. Adibi, "Abnormal event detection in crowded scenes us-ing one-class SVM," Signal, Image Video Pro-cess., 2018. [DOI:10.1007/s11760-018-1267-z]
19. [19] B. D. Devarajan, Z. Cheng, and R. J. Radke, "Camera Networks," vol. 96, no. 10, pp. 1625–1639, 2008.
20. [20] M. Piccardi, "Background subtraction techn-i-ques: a review," Vision Res., pp. 3099–3104, 2004.
21. [21] H. Cheng, Sparse Representation , Modeling and Learning in Visual Recognition. .
22. [22] X. Mo, V. Monga, R. Bala, and Z. Fan, "A joint sparsity model for video anomaly detection," Conf. Rec. - Asilomar Conf. Signals, Syst. Com-put., pp. 1969–1973, 2012. [DOI:10.1109/ACSSC.2012.6489384]
23. [23] J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210–227, 2009. [DOI:10.1109/TPAMI.2008.79] [PMID]
24. [24] A. Wagner, J. Wright, A. Ganesh, Z. Zhou, H. Mobahi, and Y. Ma, "Toward a practical face re-cognition system: Robust alignment and illu-mination by sparse representation," IEEE Trans. Pa-ttern Anal. Mach. Intell., vol. 34, no. 2, pp. 372–386, 2012. [DOI:10.1109/TPAMI.2011.112] [PMID]
25. [25] M. Planck and U. Von Luxburg, "A Tutorial on Spectral Clustering A Tutorial on Spectral Clus-tering," Stat. Comput., vol. 17, no. March, pp. 395–416, 2006.
26. [26] L. Zelnik and P. Perona, "Self-Yuning Spectral Clustering," vol. 17, no. 4.
27. [27] R. Mehran, a. Oyama, and M. Shah, "Abnormal crowd behavior detection using social force model," 2009 IEEE Conf. Comput. Vis. Pattern Recognit., no. 2, pp. 935–942, 2009.
28. [28] "PETS2006 dataset. Available: http://www.cv-g.rdg.ac.uk/PETS2006/data.html."
Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.