Illumination adaptive person reid based on teacher-student model and adversarial training

• URL: http://arxiv.org/abs/2002.01625v3
• Date: Tue, 26 May 2020 10:20:21 GMT
• Title: Illumination adaptive person reid based on teacher-student model and adversarial training
• Authors: Ziyue Zhang, Richard YD Xu, Shuai Jiang, Yang Li, Congzhentao Huang, Chen Deng
• Abstract summary: We propose a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.
• Score: 11.307571732296513
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Most existing works in Person Re-identification (ReID) focus on settings where illumination either is kept the same or has very little fluctuation. However, the changes in the illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. Its innovations are threefold: (1) A discriminative entropy loss to ensure the ReID features contain no lighting information. (2) A ReID Teacher model trained by images under "neutral" lighting conditions to guide ReID classification. (3) An illumination Teacher model trained by the differences between the illumination-adjusted and original images to guide illumination classification. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID. Experiments demonstrate that our algorithm outperforms other state-of-the-art works and particularly potent in handling images under extremely low light.

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