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Issue No.01 - Jan. (2014 vol.36)
pp: 18-32
Weixin Li , Univ. of California, San Diego, La Jolla, CA, USA
Vijay Mahadevan , Univ. of California, San Diego, La Jolla, CA, USA
Nuno Vasconcelos , Univ. of California, San Diego, La Jolla, CA, USA
ABSTRACT
The detection and localization of anomalous behaviors in crowded scenes is considered, and a joint detector of temporal and spatial anomalies is proposed. The proposed detector is based on a video representation that accounts for both appearance and dynamics, using a set of mixture of dynamic textures models. These models are used to implement 1) a center-surround discriminant saliency detector that produces spatial saliency scores, and 2) a model of normal behavior that is learned from training data and produces temporal saliency scores. Spatial and temporal anomaly maps are then defined at multiple spatial scales, by considering the scores of these operators at progressively larger regions of support. The multiscale scores act as potentials of a conditional random field that guarantees global consistency of the anomaly judgments. A data set of densely crowded pedestrian walkways is introduced and used to evaluate the proposed anomaly detector. Experiments on this and other data sets show that the latter achieves state-of-the-art anomaly detection results.
INDEX TERMS
center-surround saliency, Video analysis, surveillance, anomaly detection, crowded scene, dynamic texture,
CITATION
Weixin Li, Vijay Mahadevan, Nuno Vasconcelos, "Anomaly Detection and Localization in Crowded Scenes", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.36, no. 1, pp. 18-32, Jan. 2014, doi:10.1109/TPAMI.2013.111
REFERENCES
[1] S. Wu, B. Moore, and M. Shah, "Chaotic Invariants of Lagrangian Particle Trajectories for Anomaly Detection in Crowded Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[2] L. Kratz and K. Nishino, "Anomaly Detection in Extremely Crowded Scenes Using Spatio-Temporal Motion Pattern Models," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[3] J. Kim and K. Grauman, "Observe Locally, Infer Globally: A Space-Time MRF for Detecting Abnormal Activities with Incremental Updates," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[4] R. Mehran, A. Oyama, and M. Shah, "Abnormal Crowd Behavior Detection Using Social Force Model," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[5] Y. Benezeth, P. Jodoin, V. Saligrama, and C. Rosenberger, "Abnormal Events Detection Based on Spatio-Temporal Co-Occurences," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[6] A. Basharat, A. Gritai, and M. Shah, "Learning Object Motion Patterns for Anomaly Detection and Improved Object Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[7] T. Xiang and S. Gong, "Video Behavior Profiling for Anomaly Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 893-908, May 2008.
[8] Y. Cong, J. Yuan, and J. Liu, "Sparse Reconstruction Cost for Abnormal Event Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[9] B. Antić and B. Ommer, "Video Parsing for Abnormality Detection," Proc. IEEE Int'l Conf. Computer Vision, 2011.
[10] V. Chandola, A. Banerjee, and V. Kumar, "Anomaly Detection: A Survey," ACM Computing Surveys, vol. 41, no. 3,article 15, 2009.
[11] G. Doretto, A. Chiuso, Y. Wu, and S. Soatto, "Dynamic Textures," Int'l J. Computer Vision, vol. 51, no. 2, pp. 91-109, 2003.
[12] A. Chan and N. Vasconcelos, "Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 909-926, May 2008.
[13] D. Gao and N. Vasconcelos, "Decision-Theoretic Saliency: Computational Principles, Biological Plausibility, and Implications for Neurophysiology and Psychophysics," Neural Computation, vol. 21, no. 1, pp. 239-271, 2009.
[14] C. Stauffer and W. Grimson, "Learning Patterns of Activity Using Real-Time Tracking," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 747-757, Aug. 2000.
[15] T. Zhang, H. Lu, and S. Li, "Learning Semantic Scene Models by Object Classification and Trajectory Clustering," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2009.
[16] N. Siebel and S. Maybank, "Fusion of Multiple Tracking Algorithms for Robust People Tracking," Proc. European Conf. Computer Vision, 2006.
[17] X. Cui, Q. Liu, M. Gao, and D.N. Metaxas, "Abnormal Detection Using Interaction Energy Potentials," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[18] F. Jiang, J. Yuan, S.A. Tsaftaris, and A.K. Katsaggelos, "Anomalous Video Event Detection Using Spatiotemporal Context," Computer Vision and Image Understanding, vol. 115, no. 3, pp. 323-333, 2011.
[19] A. Adam, E. Rivlin, I. Shimshoni, and D. Reinitz, "Robust Real-Time Unusual Event Detection Using Multiple Fixed-Location Monitors," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 3, pp. 555-560, Mar. 2008.
[20] B. Zhao, L. Fei-Fei, and E. Xing, "Online Detection of Unusual Events in Videos via Dynamic Sparse Coding," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2011.
[21] D. Helbing and P. Molnár, "Social Force Model for Pedestrian Dynamics," Physical Rev. E, vol. 51, no. 5, pp. 4282-4286, 1995.
[22] O. Boiman and M. Irani, "Detecting Irregularities in Images and in Video," Int'l J. Computer Vision, vol. 74, no. 1, pp. 17-31, 2007.
[23] V. Saligrama and Z. Chen, "Video Anomaly Detection Based on Local Statistical Aggregates," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2012.
[24] R. Hamid, A. Johnson, S. Batta, A. Bobick, C. Isbell, and G. Coleman, "Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event N-Grams," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[25] D. Zhang, D. Gatica-Perez, S. Bengio, and I. McCowan, "Semi-Supervised Adapted HMMs for Unusual Event Detection," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[26] C. Stauffer and W. Grimson, "Adaptive Background Mixture Models for Real-Time Tracking," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 1999.
[27] L. Itti, C. Koch, and E. Niebur, "A Model of Saliency-Based Visual Attention for Rapid Scene Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1254-1259, Nov. 1998.
[28] R. Shumway and D. Stoffer, "An Approach to Time Series Smoothing and Forecasting Using the EM Algorithm," J. Time Series Analysis, vol. 3, no. 4, pp. 253-264, 1982.
[29] S. Roweis and Z. Ghahramani, "A Unifying Review of Linear Gaussian Models," Neural Computation, vol. 11, no. 2, pp. 305-345, 1999.
[30] S. Kullback, Information Theory and Statistics. Dover Publications, 1968.
[31] D. Gao, V. Mahadevan, and N. Vasconcelos, "On the Plausibility of the Discriminant Center-Surround Hypothesis for Visual Saliency," J. Vision, vol. 8, no. 7, pp. 1-18, 2008.
[32] V. Mahadevan and N. Vasconcelos, "Background Subtraction in Highly Dynamic Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2008.
[33] A. Chan and N. Vasconcelos, "Probabilistic Kernels for the Classification of Auto-Regressive Visual Processes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2005.
[34] J.R. Hershey and P.A. Olsen, "Approximating the Kullback Leibler Divergence between Gaussian Mixture Models," Proc. IEEE Int'l Conf. Acoustics, Speech, and Signal Processing, 2007.
[35] A.B. Chan and N. Vasconcelos, "Efficient Computation of the Kl Divergence between Dynamic Textures," Technical Report SVCL-TR-2004-02, Dept. of Electrical and Computer Eng., Univ. of California San Diego, 2004.
[36] S. Lazebnik, C. Schmid, and J. Ponce, "Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2006.
[37] A. Chan, E. Coviello, and G. Lanckriet, "Clustering Dynamic Textures with the Hierarchical EM Algorithm," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[38] J. Lafferty, A. McCallum, and F. Pereira, "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data," Proc. 18th Int'l Conf. Machine Learning, 2001.
[39] S. Kumar and M. Hebert, "Discriminative Fields for Modeling Spatial Dependencies in Natural Images," Proc. Advances in Neural Information Processing Systems, 2004.
[40] X. He, R. Zemel, and M. Carreira-Perpinán, "Multiscale Conditional Random Fields for Image Labeling," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2004.
[41] G.E. Hinton, "Training Products of Experts by Minimizing Contrastive Divergence," Neural Computation, vol. 14, pp. 1771-1800, 2002.
[42] T. Minka, "A Comparison of Numerical Optimizers for Logistic Regression," technical report, Microsoft Research, 2003.
[43] V. Mahadevan, W. Li, V. Bhalodia, and N. Vasconcelos, "Anomaly Detection in Crowded Scenes," Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2010.
[44] T.P. Kah-Kay Yung, "Example-Based Learning for View-Based Human Face Detection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 1, pp. 39-51, Jan. 1998.
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