Illumination adaptive person reid based on teacher-student model and adversarial training
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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 illumination degree may affect the robustness of a ReID algorithm significantly. To address this problem, we proposed a Two-Stream Network which can separate ReID features from lighting features so as to enhance ReID performance. Its innovations are threefold: (1)A discriminative Entropy loss is employed to ensure the ReID features contain no lighting information. (2)A ReID Teacher model is trained by images under "neutral" lighting conditions to guide ReID classification. (3)An illumination Teacher model is 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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