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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Dawei Liang Qingming Huang Hongxun Yao Shuqiang Jiang Rongrong Ji Wen Gao |
| Copyright Year | 2010 |
| Description | Author affiliation: Graduate University of Chinese Academy of Sciences, Beijing, China (Qingming Huang) || National Engineering Laboratory for Video Technology, Peking University, Beijing, China (Wen Gao) || Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China (Shuqiang Jiang) || School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China (Dawei Liang; Hongxun Yao; Rongrong Ji) |
| Abstract | Treating visual object tracking as foreground and background classification problem has attracted much attention in the past decade. Most methods adopt mean shift or brute force search to perform object tracking on the generated probability map, which is obtained from the classification results; however, performing probabilistic object tracking on the probability map is almost unexplored. This paper proposes a novel observation model which is suitable to perform this task. The observation model considers both region and boundary cues on the probability map, and can be computed very efficiently by using the integral image data structure. Extensive experiments are carried out on several challenging image sequences, which include abrupt motion change, background clutter, partial occlusion, and significant appearance change. Quantitative experiments are further performed with several related trackers on a public benchmark dataset. The experimental results demonstrate the effectiveness of the proposed approach. |
| Starting Page | 1387 |
| Ending Page | 1394 |
| File Size | 614006 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781424469840 |
| ISSN | 10636919 |
| e-ISBN | 9781424469857 |
| DOI | 10.1109/CVPR.2010.5539808 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-13 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Particle filters Particle tracking Integral equations Data structures Boosting Linear discriminant analysis Computer vision Computer science Laboratories Image sequences |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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