cvpr cvpr2013 cvpr2013-313 cvpr2013-313-reference knowledge-graph by maker-knowledge-mining
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Author: Mehrsan Javan Roshtkhari, Martin D. Levine
Abstract: We present a novel approach for video parsing and simultaneous online learning of dominant and anomalous behaviors in surveillance videos. Dominant behaviors are those occurring frequently in videos and hence, usually do not attract much attention. They can be characterized by different complexities in space and time, ranging from a scene background to human activities. In contrast, an anomalous behavior is defined as having a low likelihood of occurrence. We do not employ any models of the entities in the scene in order to detect these two kinds of behaviors. In this paper, video events are learnt at each pixel without supervision using densely constructed spatio-temporal video volumes. Furthermore, the volumes are organized into large contextual graphs. These compositions are employed to construct a hierarchical codebook model for the dominant behaviors. By decomposing spatio-temporal contextual information into unique spatial and temporal contexts, the proposed framework learns the models of the dominant spatial and temporal events. Thus, it is ultimately capable of simultaneously modeling high-level behaviors as well as low-level spatial, temporal and spatio-temporal pixel level changes.
[1] A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz. Robust real-time unusual event detection using multiple fixed-location monitors. IEEE Trans. Pattern Anal. Mach. Intell., 30(3):555–560, 2008.
[2] Y. Benezeth, P.-M. Jodoin, and V. Saligrama. Abnormality detection using low-level co-occurring events. Pattern Recogn. Lett., 32(3):423–43 1, 2011.
[3] Y. Benezeth, P. M. Jodoin, V. Saligrama, and C. Rosenberger. Abnormal events detection based on spatio-temporal co-occurences. In CVPR, pages 2458–2465, 2009.
[4] M. Bertini, A. Del Bimbo, and L. Seidenari. Multi-scale and real-
[5]
[6]
[7]
[8]
[9]
[10]
[11]
[12]
[13]
[14]
[15]
[16]
[17] time non-parametric approach for anomaly detection and localization. Compt. Vis. Image Und., 116(3):320–329, 2012. C. M. Bishop. Pattern recognition and machine learning. Springer, New York, 2006. O. Boiman and M. Irani. Detecting irregularities in images and in video. Int. J. Comput. Vision, 74(1): 17–31, 2007. Y. Cong, J. Yuan, and J. Liu. Sparse reconstruction cost for abnormal event detection. In CVPR, pages 3449–3456, 2011. E. B. Ermis, V. Saligrama, P. M. Jodoin, and J. Konrad. Motion segmentation and abnormal behavior detection via behavior clustering. In ICIP, pages 769–772, 2008. T. C. Havens, J. C. Bezdek, C. Leckie, L. O. Hall, and M. Palaniswami. Fuzzy c-means for very large data. IEEE Trans. Fuzzy Syst., PP(99): 1–1, 2012. P. Hore, L. Hall, D. Goldgof, Y. Gu, A. Maudsley, and A. Darkazanli. A scalable framework for segmenting magnetic resonance images. Journal of Signal Processing Systems, 54(1): 183–203, 2009. T. Hospedales, S. Gong, and T. Xiang. Video behaviour mining using a dynamic topic model. Int. J. Comput. Vision, pages 1–21, 2012. T. M. Hospedales, L. Jian, G. Shaogang, and X. Tao. Identifying rare and subtle behaviors: A weakly supervised joint topic model. IEEE Trans. Pattern Anal. Mach. Intell., 33(12):245 1–2464, 2011. P. Jodoin, V. Saligrama, and J. Konrad. Behavior subtraction. IEEE Trans. Image. Proc., 21(9):4244–4255, 2012. P. M. Jodoin, J. Konrad, and V. Saligrama. Modeling background activity for behavior subtraction. In Int. Conf. Distributed Smart Cameras, pages 1–10, 2008. J. Kim and K. Grauman. Observe locally, infer globally: A spacetime mrf for detecting abnormal activities with incremental updates. In CVPR, pages 2921–2928, 2009. K. Kim, T. H. Chalidabhongse, D. Harwood, and L. Davis. Real-time foregroundbackground segmentation using codebook model. RealTime Imaging, 11(3): 172–185, 2005. J. Li, S. Gong, and T. Xiang. Learning behavioural context. Int. J. Comput. Vision, 97(3):276–304, 2012.
[18] V. Mahadevan, L. Weixin, V. Bhalodia, and N. Vasconcelos. Anomaly detection in crowded scenes. In CVPR, pages 1975–1981, 2010.
[19] A. Mittal, A. Monnet, and N. Paragios. Scene modeling and change detection in dynamic scenes: A subspace approach. Compt. Vis. Image Und., 113(1):63–79, 2009.
[20] B. T. Morris and M. M. Trivedi. Trajectory learning for activity understanding: Unsupervised, multilevel, and long-term adaptive approach. IEEE Trans. Pattern Anal. Mach. Intell., 33(1 1):2287–2301, 2011.
[21] A. Oikonomopoulos, I. Patras, and M. Pantic. Spatiotemporal localization and categorization of human actions in unsegmented image sequences. IEEE Trans. Image Process., 20(4): 1126–1 140, 2011.
[22] K. Ouivirach, S. Gharti, and M. N. Dailey. Incremental behavior modeling and suspicious activity detection. Pattern Recognition, 46(3):671–680, 2013.
[23] V. Reddy, C. Sanderson, and B. C. Lovell. Improved anomaly detection in crowded scenes via cell-based analysis of foreground speed, size and texture. In CVPR Workshops, pages 55–61, 2011.
[24] E. Ricci, G. Zen, N. Sebe, and S. Messelodi. A prototype learning framework using emd: Application to complex scenes analysis. IEEE Trans. Pattern Anal. Mach. Intell., PP(99): 1–1, 2012.
[25] M. J. Roshtkhari and M. D. Levine. A multi-scale hierarchical codebook method for human action recognition in videos using a single example. In Conf. ComputerandRobot Vision, pages 182–189, 2012.
[26] V. Saligrama and C. Zhu. Video anomaly detection based on local statistical aggregates. In CVPR, pages 2112–21 19, 2012.
[27] P. Scovanner, S. Ali, and M. Shah. A 3-dimensional sift descriptor and its application to action recognition. In International conference on Multimedia, pages 357–360, Augsburg, Germany, 2007. ACM.
[28] A. Zaharescu and R. Wildes. Anomalous behaviour detection using spatiotemporal oriented energies, subset inclusion histogram comparison and event-driven processing. In ECCV, pages 563–576, 2010.
[29] X. Zhu and Z. Liu. Human behavior clustering for anomaly detection. Frontiers of Computer Science in China, 5(3):279–289, 2011.
[30] Z. Zivkovic. Improved adaptive gaussian mixture model for background subtraction. In ICPR, pages 28–31, 2004. 222666111866