Correlation filter tracking with complementary features
文献类型：会议论文
| 作者 | Wang, Wei1,2; Li, Weiguang1,2; Shi, Mingquan1
|
| 出版日期 | 2018 |
| 会议日期 | December 13, 2018 - December 16, 2018 |
| 会议地点 | Siem Reap, Cambodia |
| DOI | 10.1007/978-3-030-04224-0_42 |
| 页码 | 488-500 |
| 英文摘要 | Although Correlation Filters (CF) tracking algorithms have inherent capability to tackle various challenging scenarios individually, none of them are robust enough to handle all the challenges simultaneously. For any online tracking based on Correlation Filters, feature is one of the most important factors due to its representation power of target appearance. In this paper, we proposed a new tracking framework by integrating the advantage of complementary features to achieve robust tracking performance. The key issue of this work lies in the fact that different features respond to different tracking challenges, which also applies to deep learning features and hand-craft features. Moreover, for the tracking speed balance, we train a light-weight deep CNN features by using end-to-end learning method, which has the same Parameter magnitude as the hand-crafted features. Experimental results on OTB-2013, OTB-2015 large benchmarks datasets show that the proposed tracker performs favorably against several state-of-the-art methods. © Springer Nature Switzerland AG 2018. |
| 会议录 | 25th International Conference on Neural Information Processing, ICONIP 2018
 |
| 语种 | 英语 |
| 电子版国际标准刊号 | 16113349 |
| ISSN号 | 03029743 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/7936]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 作者单位 | 1.Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; 2.University of Chinese Academy of Sciences, Beijing, China |
| 推荐引用方式 GB/T 7714 | Wang, Wei,Li, Weiguang,Shi, Mingquan. Correlation filter tracking with complementary features[C]. 见:. Siem Reap, Cambodia. December 13, 2018 - December 16, 2018. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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