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Video Behavior Profiling for Anomaly Detection
May 2008 (vol. 30 no. 5)
pp. 893-908
ASCII Text
x 
 
Tao Xiang, Shaogang Gong, "Video Behavior Profiling for Anomaly Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 893-908, May, 2008.
 
 
BibTex
x
 
@article{ 10.1109/TPAMI.2007.70731,
author = {Tao Xiang and Shaogang Gong},
title = {Video Behavior Profiling for Anomaly Detection},
journal ={IEEE Transactions on Pattern Analysis and Machine Intelligence},
volume = {30},
number = {5},
issn = {0162-8828},
year = {2008},
pages = {893-908},
doi = {http://doi.ieeecomputersociety.org/10.1109/TPAMI.2007.70731},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - JOUR
JO - IEEE Transactions on Pattern Analysis and Machine Intelligence
TI - Video Behavior Profiling for Anomaly Detection
IS - 5
SN - 0162-8828
SP893
EP908
EPD - 893-908
A1 - Tao Xiang,
A1 - Shaogang Gong,
PY - 2008
KW - Behaviour profiling
KW - Anomaly Detection
KW - Dynamic Scene Modelling
KW - Spectral clustering
KW - FeatureSelection
KW - Dynamic Bayesian Networks.
VL - 30
JA - IEEE Transactions on Pattern Analysis and Machine Intelligence
ER -
 
This paper aims to address the problem of modelling video behaviour captured in surveillancevideos for the applications of online normal behaviour recognition and anomaly detection. A novelframework is developed for automatic behaviour profiling and online anomaly sampling/detectionwithout any manual labelling of the training dataset. The framework consists of the followingkey components: (1) A compact and effective behaviour representation method is developed basedon discrete scene event detection. The similarity between behaviour patterns are measured basedon modelling each pattern using a Dynamic Bayesian Network (DBN). (2) Natural grouping ofbehaviour patterns is discovered through a novel spectral clustering algorithm with unsupervisedmodel selection and feature selection on the eigenvectors of a normalised affinity matrix. (3) Acomposite generative behaviour model is constructed which is capable of generalising from asmall training set to accommodate variations in unseen normal behaviour patterns. (4) A run-timeaccumulative anomaly measure is introduced to detect abnormal behaviour while normal behaviourpatterns are recognised when sufficient visual evidence has become available based on an onlineLikelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normalbehaviour recognition at the shortest possible time. The effectiveness and robustness of our approachis demonstrated through experiments using noisy and sparse datasets collected from both indoorand outdoor surveillance scenarios. In particular, it is shown that a behaviour model trained usingan unlabelled dataset is superior to those trained using the same but labelled dataset in detectinganomaly from an unseen video. The experiments also suggest that our online LRT based behaviourrecognition approach is advantageous over the commonly used Maximum Likelihood (ML) methodin differentiating ambiguities among different behaviour classes observed online.

[1] L.E. Baum and T. Petrie, “Statistical Inference for Probabilistic Functions of Finite State Markov Chains,” Annals Math. Statistics, vol. 37, pp. 1554-1563, 1966.
[2] O. Boiman and M. Irani, “Detecting Irregularities in Images and in Video,” Proc. 10th IEEE Int'l Conf. Computer Vision, pp. 462-469, 2005.
[3] H. Dee and D. Hogg, “Detecting Inexplicable Behaviour,” Proc. British Machine Vision Conf., pp. 477-486, 2004.
[4] A. Dempster, N. Laird, and D. Rubin, “Maximum-Likelihood from Incomplete Data via the EM Algorithm,” J. Royal Statistical Soc. B, vol. 39, pp. 1-38, 1977.
[5] T. Duong, H. Bui, D. Phung, and S. Venkatesh, “Activity Recognition and Abnormality Detection with the Switching Hidden Semi-Markov Model,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 838-845, 2005.
[6] J. Dy, C. Brodley, A. Kak, L. Broderick, and A. Aisen, “Unsupervised Feature Selection Applied to Content-Based Retrieval of Lung Images,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, pp. 373-378, 2003.
[7] Z. Ghahramani, “Learning Dynamic Bayesian Networks,” Adaptive Processing of Sequences and Data Structures: Int`l Summer School on Neural Networks, pp. 168-197, 1998.
[8] S. Gong and T. Xiang, “Recognition of Group Activities Using Dynamic Probabilistic Networks,” Proc. Ninth IEEE Int'l Conf. Computer Vision, pp. 742-749, 2003.
[9] 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, pp. 1031-1038, 2005.
[10] A. Higgins, L. Bahler, and J. Porter, “Speaker Verification Using Randomized Phrase Prompting,” Digital Signal Processing, pp. 89-106, 1991.
[11] J. Kruskal and M. Liberman, The Symmetric Time-Warping Problem: From Continuous to Discrete. Addison-Wesley, 1983.
[12] M. Law, M.A.T. Figueiredo, and A.K. Jain, “Simultaneous Feature Selection and Clustering Using Mixture Model,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 9, pp. 1154-1166, Sept. 2004.
[13] L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition,” Proc. IEEE, vol. 77, no. 2, pp.257-286, 1989.
[14] R.J. Morris and D.C. Hogg, “Statistical Models of Object Interaction,” Int'l J. Computer Vision, vol. 37, no. 2, pp. 209-215, 2000.
[15] A. Ng, M. Jordan, and Y. Weiss, “On Spectral Clustering: Analysis and an Algorithm,” Advances in Neural Information Processing Systems, 2001.
[16] N. Oliver, B. Rosario, and A. Pentland, “A Bayesian Computer Vision System for Modelling Human Interactions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 831-843, Aug. 2000.
[17] A. Panuccio, M. Bicego, and V. Murino, “A Hidden Markov Model-Based Approach to Sequential Data Clustering,” Proc. Joint IAPR Int'l Workshop Structural, Syntactic, and Statistical Pattern Recognition, pp. 734-742, 2002.
[18] F. Porikli, “Trajectory Distance Metric Using Hidden Markov Model Based Representation,” Proc. Sixth IEEE Int'l Workshop Performance Evaluation of Tracking and Surveillance, 2002.
[19] F. Porikli and T. Haga, “Event Detection by Eigenvector Decomposition Using Object and Frame Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, pp. 114-121, 2004.
[20] S. Roberts, D. Husmeier, I. Rezek, and W. Penny, “Bayesian Approaches to Gaussian Mixture Modeling,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 20, no. 11, pp. 1133-1142, Nov. 1998.
[21] G. Schwarz, “Estimating the Dimension of a Model,” Annals Statistics, vol. 6, pp. 461-464, 1978.
[22] Y. Shan, H.S. Sawhney, and A. Pope, “Measuring the Similarity of Two Image Sequence,” Proc. Asian Conf. Computer Vision, 2004.
[23] J. Shi and J. Malik, “Normalized Cuts and Image Segmentation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 888-905, Aug. 2000.
[24] 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. 747-758, Aug. 2000.
[25] Y. Weiss, “Segmentation Using Eigenvectors: A Unifying View,” Proc. Seventh IEEE Int'l Conf. Computer Vision, pp. 975-982, 1999.
[26] J. Wilpon, L. Rabiner, C. Lee, and E. Goldman, “Automatic Recognition of Keywords in Unconstrained Speech Using Hidden Markov Models,” IEEE Trans. Acoustics, Speech, and Signal Processing, pp. 1870-1878, 1990.
[27] T. Xiang and S. Gong, “Activity Based Video Content Trajectory Representation and Segmentation,” Proc. British Machine Vision Conf., pp. 177-186, 2004.
[28] T. Xiang and S. Gong, “Video Behaviour Profiling and Abnormality Detection without Manual Labelling,” Proc. 10th IEEE Int'l Conf. Computer Vision, pp. 1238-1245, 2005.
[29] T. Xiang, S. Gong, and D. Parkinson, “Autonomous Visual Events Detection and Classification without Explicit Object-Centred Segmentation and Tracking,” Proc. British Machine Vision Conf., pp. 233-242, 2002.
[30] S. Yu and J. Shi, “Multiclass Spectral Clustering,” Proc. Ninth IEEE Int'l Conf. Computer Vision, pp. 313-319, 2003.
[31] L. Zelnik-Manor and M. Irani, “Event-Based Video Analysis,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, 2001.
[32] L. Zelnik-Manor and P. Perona, “Self-Tuning Spectral Clustering,” Advances in Neural Information Processing Systems, 2004.
[33] 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, pp. 611-618, 2005.
[34] H. Zhong, J. Shi, and M. Visontai, “Detecting Unusual Activity in Video,” Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 819-826, 2004.

Index Terms:
Behaviour profiling, Anomaly Detection, Dynamic Scene Modelling, Spectral clustering, FeatureSelection, Dynamic Bayesian Networks.
Citation:
Tao Xiang, Shaogang Gong, "Video Behavior Profiling for Anomaly Detection," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 30, no. 5, pp. 893-908, May 2008, doi:10.1109/TPAMI.2007.70731
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