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UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network

文献类型：期刊论文

作者Yao, Siyuan2; Guo, Yang2; Yan, Yanyang3; Ren, Wenqi1; Cao, Xiaochun1
刊名IEEE TRANSACTIONS ON IMAGE PROCESSING
出版日期2025
卷号34页码:3533-3546
关键词Uncertainty Location awareness Target tracking Transformers Reliability Prototypes Object tracking Visualization Accuracy Training Reliable object tracking uncertainty estimation prototype memory network memory updating
ISSN号1057-7149
DOI10.1109/TIP.2025.3559796
英文摘要Transformer-based trackers have achieved promising success and become the dominant tracking paradigm because of their accuracy and efficiency. Despite the substantial progress, most of the existing approaches handle object tracking as a deterministic coordinate regression problem, while the target localization uncertainty has been largely overlooked, which hampers trackers' ability to maintain reliable target state prediction in challenging scenarios. To address this issue, we propose UncTrack, a novel uncertainty-aware transformer-based tracker that predicts the target localization uncertainty and incorporates this uncertainty information for accurate target state inference. Specifically, UncTrack uses a transformer encoder to perform feature interactions between the template and search images. The output features are passed into an uncertainty-aware localization decoder (ULD) to coarsely predict the corner-based localization and the corresponding localization uncertainty. Then, the localization uncertainty is sent into a prototype memory network (PMN) to excavate valuable historical information to identify whether the target state prediction is reliable. To enhance the template representation, the samples with high confidence are fed back into the prototype memory bank for memory updating, which makes the tracker more robust to challenging appearance variations. Extensive experiments demonstrate that our method outperforms the state-of-the-art methods. Our code is available at https://github.com/ManOfStory/UncTrack
资助项目National Natural Science Foundation of China[62402055] ; National Natural Science Foundation of China[62302480] ; Shenzhen Science and Technology Program[KQTD20221101093559018]
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:001506596200013
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
源URL[http://119.78.100.204/handle/2XEOYT63/42341]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Cao, Xiaochun
作者单位1.Sun Yat Sen Univ, Sch Cyber Sci & Technol, Shenzhen Campus, Shenzhen 518107, Peoples R China
2.Beijing Univ Posts & Telecommun, Sch Comp Sci, Natl Pilot Software Engn Sch, Beijing 100876, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, State Key Lab Processors, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Yao, Siyuan,Guo, Yang,Yan, Yanyang,et al. UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2025,34:3533-3546.
APA Yao, Siyuan,Guo, Yang,Yan, Yanyang,Ren, Wenqi,&Cao, Xiaochun.(2025).UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network.IEEE TRANSACTIONS ON IMAGE PROCESSING,34,3533-3546.
MLA Yao, Siyuan,et al."UncTrack: Reliable Visual Object Tracking With Uncertainty-Aware Prototype Memory Network".IEEE TRANSACTIONS ON IMAGE PROCESSING 34(2025):3533-3546.

入库方式： OAI收割

来源：计算技术研究所

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