CASIA OpenIR  > 模式识别国家重点实验室  > 视频内容安全
Exploring Motion Information for Distractor Suppression in Visual Tracking
Liu, Kaiwen1,2; Gao, Jin1,2; Liu, Haowei1,2; Li, Liang4; Li, Bing1,2; Hu, Weiming1,2,3
2022-06
Conference NameIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2022
Source Publication2022
Pages1924-1932
Conference Date2022.6.19
Conference PlaceNew Orleans , United States
PublisherIEEE
Abstract

In the past few years, Siamese networks have achieved outstanding improvements in visual object tracking. However, visual distractors with similar semantics can be easily misclassified as the target by Siamese networks and may consequently result in the drift problem. Besides, the Hanning window penalty, which is generally used to suppress distractors, could fail in many challengeable scenes. Notably, most failures violate the assumption of motion continuity. Thus, in this work, we explore motion information to mitigate the drift problem in visual tracking. First, we introduce a simple linear Kalman filter to predict the bounding box of the target in the current frame, which acts as a reference for decisions. Second, an IoU-Guided penalty is assembled in the post-processing to suppress distractors effectively. It’s worth mentioning that our method is almost cost-free. We conduct numerous experimental validations and analyses of our approach on several challenging sequences and datasets. Our tracker runs at approximately 40 fps and performs well on those sequences which include the Background Clutter attribute. Finally, by simultaneously integrating the IoU-Guided penalty and the Hanning window penalty with a strong baseline tracker TransT, our method achieves favorable gains by 69.1→71.5, 65.7→66.7, 64.9→65.9 success on OTB-100, LaSOT, NFS.

Indexed ByEI
Funding ProjectKey Research Program of Frontier Sciences, CAS[QYZDJ-SSW-JSC040] ; Natural Science Foundation of China[61721004]
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/48843
Collection模式识别国家重点实验室_视频内容安全
Corresponding AuthorGao, Jin
Affiliation1.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
2.School of AI, University of Chinese Academy of Sciences
3.CAS Center for Excellence in Brain Science and Intelligence Technology
4.Beijing Institute of Basic Medical Sciences
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Corresponding Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Liu, Kaiwen,Gao, Jin,Liu, Haowei,et al. Exploring Motion Information for Distractor Suppression in Visual Tracking[C]:IEEE,2022:1924-1932.
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