CASIA OpenIR  > 智能感知与计算
Learning Instance-level Spatial-Temporal Patterns for Person Re-identification
Min Ren1,2; Lingxiao He3; Xingyu Liao3; Wu Liu3; Yunlong Wang2; Tieniu Tan2
Conference NameIEEE/CVF International Conference on Computer Vision
Conference Date2021-3-10
Conference Placevirtual

Person re-identification (Re-ID) aims to match pedes- trians under dis-joint cameras. Most Re-ID methods for- mulate it as visual representation learning and image search, and its accuracy is consequently affected greatly by the search space. Spatial-temporal information has been proven to be efficient to filter irrelevant negative sam- ples and significantly improve Re-ID accuracy. However, existing spatial-temporal person Re-ID methods are still rough and do not exploit spatial-temporal information suffi- ciently. In this paper, we propose a novel Instance-level and Spatial-Temporal Disentangled Re-ID method (InSTD), to improve Re-ID accuracy. In our proposed framework, per- sonalized information such as moving direction is explic- itly considered to further narrow down the search space. Besides, the spatial-temporal transferring probability is disentangled from joint distribution to marginal distribu- tion, so that outliers can also be well modeled. Abun- dant experimental analyses are presented, which demon- strates the superiority and provides more insights into our method. The proposed method achieves mAP of 90.8% on Market-1501 and 89.1% on DukeMTMC-reID, improv- ing from the baseline 82.2% and 72.7%, respectively. Be- sides, in order to provide a better benchmark for per- son re-identification, we release a cleaned data list of DukeMTMC-reID with this paper: https://github. com/RenMin1991/cleaned-DukeMTMC-reID/

Sub direction classification自然语言处理
planning direction of the national heavy laboratory视觉信息处理
Document Type会议论文
Affiliation1.University of Chinese Academy of Sciences
2.CRIPAC NLPR, Institute of Automation Chinese Academy of Sciences
3.JD AI Research
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Min Ren,Lingxiao He,Xingyu Liao,et al. Learning Instance-level Spatial-Temporal Patterns for Person Re-identification[C],2021.
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