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Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach
conference contribution
posted on 2012-01-01, 00:00 authored by Santu RanaSantu Rana, Quoc-Dinh Phung, S Pham, Svetha VenkateshSvetha VenkateshWe 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.
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Computer Vision, Graphics and Image Processing. Indian Conference (8th : 2012 : Mumbai, India)Pagination
1 - 8Publisher
ACM - Association for Computing MachineryLocation
Mumbai, IndiaPlace of publication
New York, N.Y.Publisher DOI
Start date
2012-12-16End date
2012-12-19ISBN-13
9781450316606Language
engPublication classification
E1 Full written paper - refereedEditor/Contributor(s)
D MukherjeeTitle of proceedings
ICVGIP 2012 : Proceedings of the 8th Indian Conference on Computer Vision, Graphics and Image ProcessingUsage metrics
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