Multi-modal abnormality detection in video with unknown data segmentation

Nguyen, Tien Vu, Phung, Dinh, Rana, Santu, Pham, Duc-Son and Venkatesh, Svetha 2012, Multi-modal abnormality detection in video with unknown data segmentation, in ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition, ICPR Organizing Committee, Tsubuka Science City, Japan, pp. 1322-1325.

Attached Files
Name Description MIMEType Size Downloads

Title Multi-modal abnormality detection in video with unknown data segmentation
Author(s) Nguyen, Tien Vu
Phung, DinhORCID iD for Phung, Dinh
Rana, SantuORCID iD for Rana, Santu
Pham, Duc-Son
Venkatesh, SvethaORCID iD for Venkatesh, Svetha
Conference name International Conference on Pattern Recognition (21st : 2012 : Tsukuba Science City, Japan)
Conference location Tsubuka Science City, Japan
Conference dates 11-15 Nov. 2012
Title of proceedings ICPR 2012 : Proceedings of 21st International Conference on Pattern Recognition
Editor(s) [Unknown]
Publication date 2012
Conference series International Conference on Pattern Recognition
Start page 1322
End page 1325
Total pages 4
Publisher ICPR Organizing Committee
Place of publication Tsubuka Science City, Japan
Keyword(s) hidden Markov models
image segmentation
video cameras
Summary This paper examines a new problem in large scale stream data: abnormality detection which is localized to a data segmentation process. Unlike traditional abnormality detection methods which typically build one unified model across data stream, we propose that building multiple detection models focused on different coherent sections of the video stream would result in better detection performance. One key challenge is to segment the data into coherent sections as the number of segments is not known in advance and can vary greatly across cameras; and a principled way approach is required. To this end, we first employ the recently proposed infinite HMM and collapsed Gibbs inference to automatically infer data segmentation followed by constructing abnormality detection models which are localized to each segmentation. We demonstrate the superior performance of the proposed framework in a real-world surveillance camera data over 14 days.
ISBN 9784990644109
Language eng
Field of Research 080104 Computer Vision
080109 Pattern Recognition and Data Mining
Socio Economic Objective 899999 Information and Communication Services not elsewhere classified
HERDC Research category E1 Full written paper - refereed
Persistent URL

Connect to link resolver
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 3 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 588 Abstract Views, 8 File Downloads  -  Detailed Statistics
Created: Fri, 24 May 2013, 12:10:43 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