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
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
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.