End-to-End Context-Aided Unicity Matching for Person Re-identification
- URL: http://arxiv.org/abs/2210.12008v1
- Date: Thu, 20 Oct 2022 07:33:57 GMT
- Title: End-to-End Context-Aided Unicity Matching for Person Re-identification
- Authors: Min Cao, Cong Ding, Chen Chen, Junchi Yan and Yinqiang Zheng
- Abstract summary: We propose an end-to-end person unicity matching architecture for learning and refining the person matching relations.
We use the samples' global context relationship to refine the soft matching results and reach the matching unicity through bipartite graph matching.
Given full consideration to real-world person re-identification applications, we achieve the unicity matching in both one-shot and multi-shot settings.
- Score: 100.02321122258638
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most existing person re-identification methods compute the matching relations
between person images across camera views based on the ranking of the pairwise
similarities. This matching strategy with the lack of the global viewpoint and
the context's consideration inevitably leads to ambiguous matching results and
sub-optimal performance. Based on a natural assumption that images belonging to
the same person identity should not match with images belonging to multiple
different person identities across views, called the unicity of person matching
on the identity level, we propose an end-to-end person unicity matching
architecture for learning and refining the person matching relations. First, we
adopt the image samples' contextual information in feature space to generate
the initial soft matching results by using graph neural networks. Secondly, we
utilize the samples' global context relationship to refine the soft matching
results and reach the matching unicity through bipartite graph matching. Given
full consideration to real-world person re-identification applications, we
achieve the unicity matching in both one-shot and multi-shot settings of person
re-identification and further develop a fast version of the unicity matching
without losing the performance. The proposed method is evaluated on five public
benchmarks, including four multi-shot datasets MSMT17, DukeMTMC, Market1501,
CUHK03, and a one-shot dataset VIPeR. Experimental results show the superiority
of the proposed method on performance and efficiency.
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