The Community for Technology Leaders
RSS Icon
Subscribe
Issue No.01 - January (2010 vol.32)
pp: 56-71
Xiaogang Wang , The Chinese University of Hong Kong, Hong Kong
Kinh Tieu , The Chinese University of Hong Kong, Hong Kong
W. Eric L. Grimson , Massachusetts Institute of Technology, Cambridge
ABSTRACT
We propose a novel approach for activity analysis in multiple synchronized but uncalibrated static camera views. In this paper, we refer to activities as motion patterns of objects, which correspond to paths in far-field scenes. We assume that the topology of cameras is unknown and quite arbitrary, the fields of views covered by these cameras may have no overlap or any amount of overlap, and objects may move on different ground planes. Using low-level cues, objects are first tracked in each camera view independently, and the positions and velocities of objects along trajectories are computed as features. Under a probabilistic model, our approach jointly learns the distribution of an activity in the feature spaces of different camera views. Then, it accomplishes the following tasks: 1) grouping trajectories, which belong to the same activity but may be in different camera views, into one cluster; 2) modeling paths commonly taken by objects across multiple camera views; and 3) detecting abnormal activities. Advantages of this approach are that it does not require first solving the challenging correspondence problem, and that learning is unsupervised. Even though correspondence is not a prerequisite, after the models of activities have been learned, they can help to solve the correspondence problem, since if two trajectories in different camera views belong to the same activity, they are likely to correspond to the same object. Our approach is evaluated on a simulated data set and two very large real data sets, which have 22,951 and 14,985 trajectories, respectively.
INDEX TERMS
Visual surveillance, activity analysis in multiple camera views, correspondence, clustering.
CITATION
Xiaogang Wang, Kinh Tieu, W. Eric L. Grimson, "Correspondence-Free Activity Analysis and Scene Modeling in Multiple Camera Views", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol.32, no. 1, pp. 56-71, January 2010, doi:10.1109/TPAMI.2008.241
REFERENCES
[1] J.W. Davis and A.F. Bobick, “The Representation and Recognition of Action Using Temporal Templates,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1997.
[2] L. Zelnik-Manor and M. Irani, “Event-Based Analysis of Video,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2001.
[3] H. Zhong, J. Shi, and M. Visontai, “Detecting Unusual Activity in Video,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2004.
[4] T. Xiang and S. Gong, “Video Behaviour Profiling and Abnormality Detection without Manual Labelling,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[5] P. Smith, N. Lobo, and M. Shah, “Temporalboost for Event Recognition,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[6] Y. Wang, T. Jiang, M.S. Drew, Z. Li, and G. Mori, “Unsupervised Discovery of Action Classes,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2006.
[7] X. Wang, X. Ma, and E. Grimson, “Unsupervised Activity Perception by Hierarchical Bayesian Models,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2007.
[8] X. Wang, X. Ma, and W.E.L. Grimson, “Unsupervised Activity Perception in Crowded and Complicated Scenes Using Hierarchical Bayesian Models,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 3, pp. 539-555, Mar. 2009.
[9] N. Johnson and D. Hogg, “Learning the Distribution of Object Trajectories for Event Recognition,” Proc. British Machine Vision Conf., 1995.
[10] C. Stauffer and W.E.L. 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.
[11] N. Oliver, B. Rosario, and A. Pentland, “A Bayesian Computer Vision System for Modeling Human Interactions,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 831-843, Aug. 2000.
[12] I. Haritaoglu, D. Harwood, and L.S. Davis, “W4: Real-Time Surveillance of People and Their Activities,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 809-830, Aug. 2000.
[13] M. Brand and V. Kettnaker, “Discovery and Segmentation of Activities in Video,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 844-851, Aug. 2000.
[14] G. Medioni, I. Cohen, F. BreAmond, S. Hongeng, and R. Nevatia, “Event Detection and Analysis from Video Streams,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 8, pp. 873-889, Aug. 2001.
[15] S. Honggeng and R. Nevatia, “Multi-Agent Event Recognition,” Proc. IEEE Int'l Conf. Computer Vision, 2001.
[16] T.T. Truyen, D.Q. Phung, H.H. Bui, and S. Venkatesh, “Adaboost.mrf: Boosted Markov Random Forests and Application to Multilevel Activity Recognition,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2006.
[17] X. Wang, K. Tieu, and W.E.L. Grimson, “Learning Semantic Scene Models by Trajectory Analysis,” Proc. European Conf. Computer Vision, 2006.
[18] T. Xiang and S. Gong, “Beyond Tracking: Modelling Activity and Understanding Behaviour,” Int'l J. Computer Vision, vol. 67, pp. 21-51, 2006.
[19] W. Hu, X. Xiao, Z. Fu, D. Xie, T. Tan, and S. Maybank, “A System for Learning Statistical Motion Patterns,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 9, pp. 1450-1464, Sept. 2006.
[20] E. Keogh and M. Pazzani, “Scaling Up Dynamic Time,” Proc. ACM SIGKDD, 2000.
[21] D. Makris and T. Ellis, “Path Detection in Video Surveillance,” Image Vision and Computation, vol. 20, pp. 859-903, 2002.
[22] I. Junejo, O. Javed, and M. Shah, “Multi Feature Path Modeling for Video Surveillance,” Proc. IEEE Int'l Conf. Pattern Recognition, 2004.
[23] F.M. Porikli and T. Haga, “Event Detection by Eigenvector Decomposition Using Object and Frame Features,” Proc. IEEE Conf. Computer Vision and Pattern Recognition Workshop, 2004.
[24] Z. Fu, W. Hu, and T. Tan, “Similarity Based Vehicle Trajectory Clustering and Anomaly Detection,” Proc. IEEE Int'l Conf. Image Processing, 2005.
[25] Z. Zhang, K. Huang, and T. Tan, “Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes,” Proc. IEEE Int'l Conf. Pattern Recognition, 2006.
[26] R. Kaucic, A. Perera, G. Brooksby, J. Kaufhold, and A. Hoogs, “A Unified Framework for Tracking through Occlusions and across Sensor Gaps,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2005.
[27] D. Makris and T. Ellis, “Automatic Learning of an Activity-Based Semantic Scene Model,” Proc. IEEE Conf. Advanced Video and Signal Based Surveillance, 2003.
[28] J. Fernyhough, A. Cohn, and D. Hogg, “Generation of Semantic Regions from Image Sequences,” Proc. European Conf. Computer Vision, 1996.
[29] I. Junejo and H. Foroosh, “Trajectory Rectification and Path Modeling for Video Surveillance,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[30] T. Huang and S. Russell, “Object Identification in a Bayesian Context,” Proc. Int'l Joint Conf. Artificial Intelligence, 1997.
[31] Q. Cai and J.K. Aggarwal, “Tracking Human Motion in Structured Environments Using a Distributed-Camera System,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 21, no. 11, pp. 1241-1247, Nov. 1999.
[32] V. Kettnaker and R. Zabih, “Bayesian Multi-Camera Surveillance,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 1999.
[33] L. Lee, R. Romano, and G. Stein, “Monitoring Activities from Multiple Video Streams: Establishing a Common Coordinate Frame,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 8, pp. 758-768, Aug. 2000.
[34] R.T. Collins, A.J. Lipton, H. Fujiyoshi, and T. Kanade, “Algorithms for Cooperative Multisensor Surveillance,” Proc. IEEE, vol. 89, no. 10, pp. 1456-1477, Oct. 2001.
[35] S. Khan and M. Shah, “Consistent Labeling of Tracked Objects in Multiple Cameras with Overlapping Fields of View,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 10, pp. 1355-1360, Oct. 2003.
[36] J. Kang, I. Cohen, and G. Medioni, “Continuous Tracking within and across Camera Streams,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2003.
[37] O. Javed, Z. Rasheed, K. Shafique, and M. Shah, “Tracking across Multiple Cameras with Disjoint Views,” Proc. IEEE Int'l Conf. Computer Vision, 2003.
[38] C. Stauffer and K. Tieu, “Automated Multi-Camera Planar Tracking Correspondence Modeling,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2003.
[39] D. Makris, T. Ellis, and J. Black, “Bridging the Gaps between Cameras,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2004.
[40] A. Rahimi, B. Dunagan, and T. Darrell, “Simultaneous Calibration and Tracking with a Network of Non-Overlapping Sensors,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2004.
[41] Y. Shan, H. Sawhney, and R. Kumar, “Unsupervised Learning of Discriminative Edge Measures for Vehicle Matching between Non-Overlapping Cameras,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2005.
[42] O. Javed, K. Shafique, and M. Shah, “Appearance Modeling for Tracking in Multiple Non-Overlapping Cameras,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2005.
[43] Y. Shan, H. Sawhney, and R. Kumar, “Vehicle Identification between Non-Overlapping Cameras without Direct Feature Matching,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[44] K. Tieu, G. Dalley, and E. Grimson, “Inference of Non-Overlapping Camera Network Topology by Measuring Statistical Dependence,” Proc. IEEE Int'l Conf. Computer Vision, 2005.
[45] N. Gheissari, T.B. Sebastian, J. Rittscher, and R. Hartley, “Person Reidentification Using Spatiotemporal Appearance,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2006.
[46] G. Unal, A. Yezzi, S. Soatto, and G. Slabaugh, “A Variational Approach to Problems in Calibration of Multiple Cameras,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 8, pp.1322-1338, Aug. 2007.
[47] Y.A. Sheikh and M. Shah, “Trajectory Association across Multiple Airborne Cameras,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp. 361-367, Feb. 2008.
[48] F. Fleuret, J. Berclaz, R. Lengagne, and P. Fua, “Multicamera People Tracking with a Probabilistic Occupancy Map,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 30, no. 2, pp.267-282, Feb. 2008.
[49] B. Triggs, “Camera Pose and Calibration from 4 or 5 Known 3d Points,” Proc. IEEE Int'l Conf. Computer Vision, 1999.
[50] P. Gurdjos and P. Sturm, “Methods and Geometry for Plane-Based Self-Calibration,” Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition, 2003.
[51] M. Brown and D. Lowe, “Recognising Panoramas,” Proc. IEEE Int'l Conf. Computer Vision, 2003.
[52] X. Wang, G. Doretto, T. Sebastian, J. Rittscher, and P. Tu, “Shape and Appearance Context Modeling,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[53] M.R. Garey and D.S. Johnson, Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, 1979.
[54] T. Hofmann, “Probabilistic Latent Semantic Analysis,” Proc. Conf. Uncertainty in Artificial Intelligence, 1999.
[55] D.M. Blei, A.Y. Ng, and M.I. Jordan, “Latent Dirichlet Allocation,” J. Machine Learning Research, vol. 3, pp. 993-1022, 2003.
[56] H.W. Kuhn, “Variants of the Hungarian Method for Assignment Problems,” Naval Research Logistics Quarterly, vol. 3, pp. 253-258, 1956.
[57] Y.W. Teh, M.I. Jordan, M.J. Beal, and D.M. Blei, “Hierarchical Dirichlet Process,” J. Am. Statistical Assoc., 2006.
18 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool