Camera Style Guided Feature Generation for Person Re identification
Camera variance has always been a troublesome matter in person re identification (re ID) Recently, more and more interests have grown in alleviating the camera variance problem by data augmentation through generative models. However, these methods, mostly based on image level generative adversarial networks (GANs) require huge computational power during the training process of generative models. In this paper, we propose to solve the person re ID problem by adopting a feature level camera style guided GAN, which can serve as an intra class augmentation method to enhance the model robustness against camera variance. Specifically, the proposed method makes camera style transfer on input features while preserving the corresponding identity information. Moreover, the training process can be directly injected into the re ID task in an end to end manner, which means we can deploy our methods with much less time and space costs. Experiments show the validity of the generative model and its benefits towards re ID performance on Market 1501 and DukeMTMC reID datasets.
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