Long-Term Cloth-Changing Person Re-identification
Xuelin Qian, Wenxuan Wang, Li Zhang, Fangrui Zhu, Yanwei Fu, Tao, Xiang, Yu-Gang Jiang, Xiangyang Xue

TL;DR
This paper introduces a new long-term cloth-changing person re-identification dataset and proposes a shape-based deep learning method to improve matching accuracy despite clothing changes over time.
Contribution
The work provides the first large-scale dataset for long-term cloth-changing Re-ID and develops a novel shape-focused model to address clothing variability.
Findings
The proposed model outperforms existing methods on the new LTCC dataset.
Shape features are more reliable than clothing appearance for long-term Re-ID.
The dataset enables future research on cloth-changing person re-identification.
Abstract
Person re-identification (Re-ID) aims to match a target person across camera views at different locations and times. Existing Re-ID studies focus on the short-term cloth-consistent setting, under which a person re-appears in different camera views with the same outfit. A discriminative feature representation learned by existing deep Re-ID models is thus dominated by the visual appearance of clothing. In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes. This problem, termed Long-Term Cloth-Changing (LTCC) Re-ID is much understudied due to the lack of large scale datasets. The first contribution of this work is a new LTCC dataset containing people captured over a long period of time with frequent clothing changes. As a…
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Taxonomy
TopicsVideo Surveillance and Tracking Methods · Face recognition and analysis · Human Pose and Action Recognition
