Collaborative Correlation Tracking
文献类型：会议论文
| 作者 | Zhu, Guibo1 ; Wang, Jinqiao1; Wu, Yi2; Lu, Hanqing1
|
| 出版日期 | 2015 |
| 会议日期 | September 7-10 |
| 会议地点 | Swansea, UK |
| 关键词 | Visual Tracking Collaborative Correlation Tracking |
| 英文摘要 | Correlation filter based tracking has attracted many researchers’ attention in recent years for high efficiency and robustness. Most existing works focus on exploiting different characteristics with correlation filters for visual tracking, e.g. circulant structure, kernel trick, effective feature representation and context information. However, how to handle the scale variation and the model drift is still an open problem. In this paper, we propose a collaborative correlation tracker to deal with the above problems. Firstly, we extend the correlation tracking filter by embedding the scale factor into the kernelized matrix to handle the scale variation. Then a novel long-term CUR filter for detection is learnt efficiently with random sampling to alleviate model drift by detecting effective object candidates in the collaborative tracker. In this way, the proposed approach could estimate the object state accurately and handle the model drift problem effectively. Extensive experiments show the superiority of the proposed method. |
| 会议录 | In proceedings of British Machine Computer Vision
 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/11756]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
| 通讯作者 | Wang, Jinqiao |
| 作者单位 | 1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.B-DAT & CICAEET, School of Information & Control, Nanjing University of Information Science and Technology, Nanjing, 210044, Jiangsu, China |
| 推荐引用方式 GB/T 7714 | Zhu, Guibo,Wang, Jinqiao,Wu, Yi,et al. Collaborative Correlation Tracking[C]. 见:. Swansea, UK. September 7-10. |
入库方式： OAI收割
来源：自动化研究所
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