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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ce Li Zhenjun Han Qixiang Ye Jianbin Jiao |
| Copyright Year | 2011 |
| Abstract | This paper proposes a new method for abnormal behavior detection in surveillance videos via sparse reconstruction analysis. The motion trajectories of objects are firstly defined as fixed-length parametric vectors based on approximating cubic B-spline curves. Then the vectors are classified as behavior patterns and finally distinguished between normal and abnormal behaviors based on sparse reconstruction analysis, in which a classifier is constructed with sparse linear reconstruction coefficients by computing L1-norm minimization and sparse reconstruction residuals learning from labeled training samples. Experimental results on public dataset show the effectiveness of the proposed approach. |
| Starting Page | 807 |
| Ending Page | 810 |
| File Size | 655368 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781457715600 |
| DOI | 10.1109/ICIG.2011.104 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-08-12 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Training L1-norm minimization trajectory representation sparse reconstruction analysis Surveillance Support vector machine classification behavior detection Minimization Trajectory Image reconstruction Spline |
| Content Type | Text |
| Resource Type | Article |
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