Part-based long-term tracking via multiple correlation filters
文献类型：会议论文
| 作者 | Chen HY(陈宏宇)1,2,3,4,5 ; Luo HB(罗海波)1,2,3,5; Hui B(惠斌)1,2,3,5; Chang Z(常铮)1,2,3,5; He M(何淼)1,2,3,4,5
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| 出版日期 | 2020 |
| 会议日期 | December 3-5, 2019 |
| 会议地点 | Beijing, China |
| 关键词 | computer vision target tracking part-based tracking correlation filter long-term tracking |
| 页码 | 1-6 |
| 英文摘要 | Compared with short-term tracking, long-term tracking is a more challenging task. It need to have the ability to capture the target in long-term sequences, and undergo the frequent disappearance and re-appearance of target. Therefore, long-term tracking is much closer to realistic tracking system. But few long-term tracking algorithms have been done and few promising performance have been shown. In this paper, we focus on long-term visual tracking framework based on parts with multiple correlation filters. First of all, multiple correlation filters have been applied to locate the target collaboratively and address the partial occlusion issue in a local search region. Based on the confidence score between the consecutive frames, our tracker determines whether the current tracking result is reliable or not. In addition, an online SVM detector is trained by sampling positive and negative samples around the reliable tracking target. The local-to-global search region strategy is adopted to adapt the short-term tracking and long-term tracking. When heavy occlusion or out-of-view causes the tracking failure, the re-detection module will be activated. Extensive experimental results on tracking datasets show that our proposed tracking method performs favorably against state-of-the-art methods in terms of accuracy, and robustness. |
| 源文献作者 | Chinese Society for Optical Engineering ; Science and Technology on Low-light-level Night Vision Laboratory |
| 产权排序 | 1 |
| 会议录 | 6th Symposium on Novel Optoelectronic Detection Technology and Applications
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| 会议录出版者 | SPIE |
| 会议录出版地 | Bellingham, USA |
| 语种 | 英语 |
| ISSN号 | 0277-786X |
| ISBN号 | 978-1-5106-3704-7 |
| WOS记录号 | WOS:000672624800253 |
| 源URL | [http://ir.sia.cn/handle/173321/27641]  |
| 专题 | 沈阳自动化研究所_光电信息技术研究室 |
| 通讯作者 | Chen HY(陈宏宇) |
| 作者单位 | 1.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China 2.Key Lab of Image Understanding and Computer Vision, Liaoning province, Shenyang 110016, China 3.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China 4.University of Chinese Academy of Sciences, Beijing 100049, China 5.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China |
| 推荐引用方式 GB/T 7714 | Chen HY,Luo HB,Hui B,et al. Part-based long-term tracking via multiple correlation filters[C]. 见:. Beijing, China. December 3-5, 2019. |
入库方式： OAI收割
来源：沈阳自动化研究所
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