Journal
2021 IEEE/CVF INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV 2021)
Volume -, Issue -, Pages 14910-14919Publisher
IEEE
DOI: 10.1109/ICCV48922.2021.01466
Keywords
-
Funding
- National Natural Science Foundation of China [62006225, 61806197, 62071468, U1836217]
- Strategic Priority Research Program of Chinese Academy of Sciences [XDA27040700]
- Tianjin Science and Technology Plan Project [17ZXRGGX00110]
Ask authors/readers for more resources
The proposed method improves person re-identification accuracy by explicitly considering personalized information and disentangling spatial-temporal transferring probability, resulting in a more accurate and efficient Re-ID performance.
Person re-identification (Re-ID) aims to match pedestrians under dis-joint cameras. Most Re-ID methods formulate 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 samples and significantly improve Re-ID accuracy. However, existing spatial-temporal person Re-ID methods are still rough and do not exploit spatial-temporal information sufficiently. 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, personalized information such as moving direction is explicitly considered to further narrow down the search space. Besides, the spatial-temporal transferring probability is disentangled from joint distribution to marginal distribution, so that outliers can also be well modeled. Abundant experimental analyses are presented, which demonstrates 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, improving from the baseline 82.2% and 72.7%, respectively. Besides, in order to provide a better benchmark for person re-identification, we release a cleaned data list of DukeMTMC-reID with this paper: https://github. com/RenMin1991/cleaned-DukeMTMC-reID/
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available