Online Detection of Abnormal Events in Video Streams
We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image i...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2013-01-01
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| Series: | Journal of Electrical and Computer Engineering |
| Online Access: | http://dx.doi.org/10.1155/2013/837275 |
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| _version_ | 1832551733143797760 |
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| author | Tian Wang Jie Chen Hichem Snoussi |
| author_facet | Tian Wang Jie Chen Hichem Snoussi |
| author_sort | Tian Wang |
| collection | DOAJ |
| description | We propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of
an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique. |
| format | Article |
| id | doaj-art-547ce7a980464e22b4015dfaefbfa5fb |
| institution | Kabale University |
| issn | 2090-0147 2090-0155 |
| language | English |
| publishDate | 2013-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Electrical and Computer Engineering |
| spelling | doaj-art-547ce7a980464e22b4015dfaefbfa5fb2025-02-03T06:00:36ZengWileyJournal of Electrical and Computer Engineering2090-01472090-01552013-01-01201310.1155/2013/837275837275Online Detection of Abnormal Events in Video StreamsTian Wang0Jie Chen1Hichem Snoussi2Institut Charles Delaunay, LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, 10004 Troyes, FranceObservatoire de la Côte d'Azur, UMR 7293 CNRS, University of Nice Sophia-Antipolis, 06108 Nice, FranceInstitut Charles Delaunay, LM2S-UMR STMR 6279 CNRS, University of Technology of Troyes, 10004 Troyes, FranceWe propose an algorithm to handle the problem of detecting abnormal events, which is a challenging but important subject in video surveillance. The algorithm consists of an image descriptor and online nonlinear classification method. We introduce the covariance matrix of the optical flow and image intensity as a descriptor encoding moving information. The nonlinear online support vector machine (SVM) firstly learns a limited set of the training frames to provide a basic reference model then updates the model and detects abnormal events in the current frame. We finally apply the method to detect abnormal events on a benchmark video surveillance dataset to demonstrate the effectiveness of the proposed technique.http://dx.doi.org/10.1155/2013/837275 |
| spellingShingle | Tian Wang Jie Chen Hichem Snoussi Online Detection of Abnormal Events in Video Streams Journal of Electrical and Computer Engineering |
| title | Online Detection of Abnormal Events in Video Streams |
| title_full | Online Detection of Abnormal Events in Video Streams |
| title_fullStr | Online Detection of Abnormal Events in Video Streams |
| title_full_unstemmed | Online Detection of Abnormal Events in Video Streams |
| title_short | Online Detection of Abnormal Events in Video Streams |
| title_sort | online detection of abnormal events in video streams |
| url | http://dx.doi.org/10.1155/2013/837275 |
| work_keys_str_mv | AT tianwang onlinedetectionofabnormaleventsinvideostreams AT jiechen onlinedetectionofabnormaleventsinvideostreams AT hichemsnoussi onlinedetectionofabnormaleventsinvideostreams |