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Centre for Pattern Recognition and Data Analytics
School of Information Technology
Locked Bag 20000
GEELONG VIC 3220
S. Rana, D.Q. Phung, and S. Pham, S. Venkatesh. Large-Scale Statistical Modeling of Motion Patterns: A Bayesian Nonparametric Approach. In Proceedings of IEEE Indian Conference on Vision, Graphics and Image Processing, 2012.
We propose a novel framework for large-scale scene understanding in static camera surveillance. Our techniques combine fast rank-1 constrained robust PCA to compute the foreground, with non-parametric Bayesian models for inference. Clusters are extracted in foreground patterns using a joint multinomial+Gaussian Dirichlet process model (DPM). Since the multinomial distribution is normalized, the Gaussian mixture distinguishes between similar spatial patterns but di erent activity levels (eg. car vs bike). We propose a modication of the decayed MCMC technique for incremental inference, providing the ability to discover theoretically unlimited patterns in unbounded video streams. A promising by-product of our framework is online, abnormal activity detection. A benchmark video and two surveillance videos, with the longest being 140 hours long are used in our experiments. The patterns discovered are as informative as existing scene understanding algorithms. However, unlike existing work, we achieve near real-time execution and encouraging performance in abnormal activity detection.
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27th February 2015