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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Suha Kwak Woonhyun Nam Bohyung Han Joon Hee Han |
| Copyright Year | 2011 |
| Description | Author affiliation: Department of Computer Science and Engineering, POSTECH, Korea (Suha Kwak; Woonhyun Nam; Bohyung Han; Joon Hee Han) |
| Abstract | We propose a novel algorithm to detect occlusion for visual tracking through learning with observation likelihoods. In our technique, target is divided into regular grid cells and the state of occlusion is determined for each cell using a classifier. Each cell in the target is associated with many small patches, and the patch likelihoods observed during tracking construct a feature vector, which is used for classification. Since the occlusion is learned with patch likelihoods instead of patches themselves, the classifier is universally applicable to any videos or objects for occlusion reasoning. Our occlusion detection algorithm has decent performance in accuracy, which is sufficient to improve tracking performance significantly. The proposed algorithm can be combined with many generic tracking methods, and we adopt L1 minimization tracker to test the performance of our framework. The advantage of our algorithm is supported by quantitative and qualitative evaluation, and successful tracking and occlusion reasoning results are illustrated in many challenging video sequences. |
| Starting Page | 1551 |
| Ending Page | 1558 |
| File Size | 2606644 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457711015 |
| ISSN | 15505499 |
| e-ISBN | 9781457711022 |
| DOI | 10.1109/ICCV.2011.6126414 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-11-06 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Cognition Vectors Training Minimization Videos |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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