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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Xiaofeng Ding Chengrong Huang Fengchen Huang Lizhong Xu Xiao-fang Li |
| Copyright Year | 2010 |
| Description | Author affiliation: College of Computer and Information, Hohai University, Nanjing, 210098, China (Xiaofeng Ding; Fengchen Huang; Lizhong Xu; Xiao-fang Li) || School of Computer Engineering, Nanjing, Institute of Technology, 211167, China (Chengrong Huang) |
| Abstract | Covariance features enabled efficient fusion of different type of image features have low dimensions and covariance-based object tracking has been proved robust, versatile for a modest computational cost. In this paper, a method combined Monte Carlo method and covariance features is proposed. Monte Carlo method is used to determine the scope of the search target at the region level. Covariance features are used to model the objects appearance at the object level. An improved object matching and occlusion handling strategies are given, which are followed by an appearance model update method. Experiments show our approach is robust and effective for tracking the object with irregular movement and partial occlusions. |
| Starting Page | 1802 |
| Ending Page | 1805 |
| File Size | 421714 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424451951 |
| ISSN | 19483457 |
| DOI | 10.1109/ICCA.2010.5524120 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-06-09 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Target tracking Robustness Kernel Histograms Computational efficiency Covariance matrix Object detection Fuses Particle filters Particle tracking |
| Content Type | Text |
| Resource Type | Article |
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