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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lun Wu Yongtian Wang Yue Liu |
| Copyright Year | 2010 |
| Description | Author affiliation: School of Optoelectronics, Beijing Institute of Technology, 100081, China (Lun Wu; Yongtian Wang; Yue Liu) |
| Abstract | Background subtraction, and tracking are the first processing steps in video surveillance applications. Prior work on background modeling has mainly focused on model representation for each pixel, further more, it also need longer training sequences in which no much more moving objects. Here, we introduce a new background modeling approach based on R-PCA for the background subtraction, which can obtain better results from short video contains lots of motions. After that, we use morphological operations to filter out the motion noise, and then combine appearance-based Mean Shift and particle filter tracker for accurately moving target tracking. The proposed algorithm is not only avoiding the degeneracy problem in the particle filter, and also overcoming lost track problem when occlusion occurs. Experiments on real videos captured at different situation shows the effectiveness of the proposed system. |
| Starting Page | 244 |
| Ending Page | 247 |
| File Size | 724734 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424479573 |
| e-ISBN | 9781424479580 |
| DOI | 10.1109/CMCE.2010.5609864 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-08-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tracking Computational modeling Background subtraction Filtering algorithms R-PCA |
| Content Type | Text |
| Resource Type | Article |
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