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 focus...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
IEEE
2012
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| Subjects: | |
| Online Access: | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460383 http://hdl.handle.net/20.500.11937/42528 |
| _version_ | 1848756444107636736 |
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| author | Nguyen, T. Rana, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author2 | N/A |
| author_facet | N/A Nguyen, T. Rana, S. Phung, D. Pham, DucSon Venkatesh, S. |
| author_sort | Nguyen, T. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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 pro-posed 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. |
| first_indexed | 2025-11-14T09:12:17Z |
| format | Conference Paper |
| id | curtin-20.500.11937-42528 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:12:17Z |
| publishDate | 2012 |
| publisher | IEEE |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-425282017-01-30T15:00:08Z Multi-modal abnormality detection in video with unknown data segmentation Nguyen, T. Rana, S. Phung, D. Pham, DucSon Venkatesh, S. N/A vectors detectors surveillance hidden Markov models computational modeling data models cameras 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 pro-posed 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. 2012 Conference Paper http://hdl.handle.net/20.500.11937/42528 http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460383 IEEE restricted |
| spellingShingle | vectors detectors surveillance hidden Markov models computational modeling data models cameras Nguyen, T. Rana, S. Phung, D. Pham, DucSon Venkatesh, S. Multi-modal abnormality detection in video with unknown data segmentation |
| title | Multi-modal abnormality detection in video with unknown data segmentation |
| title_full | Multi-modal abnormality detection in video with unknown data segmentation |
| title_fullStr | Multi-modal abnormality detection in video with unknown data segmentation |
| title_full_unstemmed | Multi-modal abnormality detection in video with unknown data segmentation |
| title_short | Multi-modal abnormality detection in video with unknown data segmentation |
| title_sort | multi-modal abnormality detection in video with unknown data segmentation |
| topic | vectors detectors surveillance hidden Markov models computational modeling data models cameras |
| url | http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6460383 http://hdl.handle.net/20.500.11937/42528 |