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Recurrent Deep Attention Network for Person Re-Identification

Person re-identification (re-id) is an important task in video surveillance. It is challenging due to the appearance of person varying a wide range across non-overlapping camera views. Recent years, attention-based models are introduced to learn discriminative representation. In this paper, we consider the attention selection in a natural way as like human moving attention on different parts of the visual field for person re-id. In concrete, we propose a Recurrent Deep Attention Network (RDAN) with an attention selection mechanism based on reinforcement learning. The proposed RDAN aims to progressively observe the identity-sensitive regions to build up the representation of individuals. Extensive experiments on three person reid benchmarks Market-1501, DukeMTMC-reID, and CUHK03-NP demonstrate the proposed method can achieve competitive performance.