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Part-based long-term tracking via multiple correlation filters
Chen HY(陈宏宇)1,2,3,4,5; Luo HB(罗海波)1,2,4,5; Hui B(惠斌)1,2,4,5; Chang Z(常铮)1,2,4,5; He M(何淼)1,2,3,4,5
Department光电信息技术研究室
Conference Name6th Symposium on Novel Optoelectronic Detection Technology and Applications
Conference DateDecember 3-5, 2019
Conference PlaceBeijing, China
Author of SourceChinese Society for Optical Engineering ; Science and Technology on Low-light-level Night Vision Laboratory
Source Publication6th Symposium on Novel Optoelectronic Detection Technology and Applications
PublisherSPIE
Publication PlaceBellingham, USA
2020
Pages1-6
Indexed ByEI
EI Accession number20203709173673
Contribution Rank1
ISSN0277-786X
ISBN978-1-5106-3704-7
Keywordcomputer vision target tracking part-based tracking correlation filter long-term tracking
AbstractCompared 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.
Language英语
Document Type会议论文
Identifierhttp://ir.sia.cn/handle/173321/27641
Collection光电信息技术研究室
Corresponding AuthorChen HY(陈宏宇)
Affiliation1.Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
4.Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Science, Shenyang 110016, China
5.Key Lab of Image Understanding and Computer Vision, Liaoning province, Shenyang 110016, China
Recommended Citation
GB/T 7714
Chen HY,Luo HB,Hui B,et al. Part-based long-term tracking via multiple correlation filters[C]//Chinese Society for Optical Engineering, Science and Technology on Low-light-level Night Vision Laboratory. Bellingham, USA:SPIE,2020:1-6.
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