Unsupervised domain-adaptive person re-identification with multi-camera constraints

• URL: http://arxiv.org/abs/2210.13999v1
• Date: Tue, 25 Oct 2022 13:12:28 GMT
• Title: Unsupervised domain-adaptive person re-identification with multi-camera constraints
• Authors: S. Takeuchi, F. Li, S. Iwasaki, J. Ning, G. Suzuki
• Abstract summary: We propose an environment-constrained adaptive network for reducing the domain gap. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. We develop a method that appropriately selects a person from the pair that contributes to the performance improvement.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Person re-identification is a key technology for analyzing video-based human behavior; however, its application is still challenging in practical situations due to the performance degradation for domains different from those in the training data. Here, we propose an environment-constrained adaptive network for reducing the domain gap. This network refines pseudo-labels estimated via a self-training scheme by imposing multi-camera constraints. The proposed method incorporates person-pair information without person identity labels obtained from the environment into the model training. In addition, we develop a method that appropriately selects a person from the pair that contributes to the performance improvement. We evaluate the performance of the network using public and private datasets and confirm the performance surpasses state-of-the-art methods in domains with overlapping camera views. To the best of our knowledge, this is the first study on domain-adaptive learning with multi-camera constraints that can be obtained in real environments.

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