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

2020-02-13 07:32:22
Ziyue Zhang, Richard YD Xu, Shuai Jiang, Yang Li, Congzhentao Huang, Chen Deng
arXiv_CV
           
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.

Abstract (translated)

URL

https://arxiv.org/abs/2002.01625

PDF

https://arxiv.org/pdf/2002.01625.pdf

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