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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Tianyu Yang Baopu Li Chao Hu Meng, M.Q.-H. |
| Copyright Year | 2013 |
| Description | Author affiliation: Ningbo Inst. of Technol., Zhejiang Univ., Ningbo, China (Chao Hu) || Dept. of Electron. Eng., Chinese Univ. of Hong Kong, Hong Kong, China (Meng, M.Q.-H.) || Guangdong Provincial Key Lab. of Robot. & Intell. Syst., Shenzhen Inst. of Adv. Technol., Shenzhen, China (Tianyu Yang; Baopu Li) |
| Abstract | This paper introduces a novel tracking framework for robots that can adapt various appearance changes of object and also owns the ability of reacquisition after drift. Two classifiers, LaRank and Online Random Ferns, are adopted to realize this tracking algorithm. The former one maintains the adaptive tracking using a Condensation-based method with an online support vector machine (SVM) as observation model, which also provides the reliable image patch samples to detector for updating. The other one is in charge of the task of detection in order to redetect the object when the target drifts. We also present a refinement strategy to improve the tracker's performance by discarding the support vector corresponding to possible wrong updates by a matching template after re-initialization. The experiments on benchmark dataset compare our tracking method with several other state-of-the-art algorithms, demonstrating a promising performance of the proposed framework. |
| Starting Page | 4755 |
| Ending Page | 4760 |
| File Size | 1052181 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781467356411 |
| ISSN | 10504729 |
| e-ISBN | 9781467356435 |
| DOI | 10.1109/ICRA.2013.6631254 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2013-05-06 |
| Publisher Place | Germany |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Laboratories TV Reliability Videos |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Control and Systems Engineering Electrical and Electronic Engineering Software |
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