Large-scale statistical modeling of motion patterns: A Bayesian nonparametric approach

Santu Rana, Dinh Phung, Sonny Pham, Svetha Venkatesh

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

9 Citations (Scopus)


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 different activity levels (eg. car vs bike). We propose a modification 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.

Original languageEnglish
Title of host publicationProceedings - 8th Indian Conference on Computer Vision, Graphics and Image Processing, ICVGIP 2012
Publication statusPublished - 1 Dec 2012
Externally publishedYes
EventIndian Conference on Computer Vision, Graphics and Image Processing 2012 - Mumbai, India
Duration: 16 Dec 201219 Dec 2012

Publication series

NameACM International Conference Proceeding Series


ConferenceIndian Conference on Computer Vision, Graphics and Image Processing 2012
Abbreviated titleICVGIP 2012

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