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

Rana, Santu, Phung, Dinh, Pham, Sonny and Venkatesh, Svetha 2012, Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach, in ICVGIP 2012 : Proceedings of the 8th Indian Conference on Computer Vision, Graphics and Image Processing, ACM - Association for Computing Machinery, New York, N.Y., pp. 1-8.

Attached Files
Name Description MIMEType Size Downloads

Title Large-scale statistical modeling of motion patterns : a Bayesian nonparametric approach
Author(s) Rana, Santu
Phung, Dinh
Pham, Sonny
Venkatesh, Svetha
Conference name Computer Vision, Graphics and Image Processing. Indian Conference (8th : 2012 : Mumbai, India)
Conference location Mumbai, India
Conference dates 16 -19 Dec. 2012
Title of proceedings ICVGIP 2012 : Proceedings of the 8th Indian Conference on Computer Vision, Graphics and Image Processing
Editor(s) Mukherjee, Dipti Prasad
Publication date 2012
Conference series Computer Vision, Graphics and Image Processing Indian Conference
Start page 1
End page 8
Total pages 8
Publisher ACM - Association for Computing Machinery
Place of publication New York, N.Y.
Summary 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.
ISBN 9781450316606
Language eng
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 890205 Information Processing Services (incl. Data Entry and Capture)
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30051790

Document type: Conference Paper
Collection: Centre for Pattern Recognition and Data Analytics
Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Access Statistics: 53 Abstract Views, 4 File Downloads  -  Detailed Statistics
Created: Thu, 04 Apr 2013, 13:01:12 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.