Person re-identification (re-id) is an important task in video surveillance. It is challenging due to the appearance of the person varying a wide range across non-overlapping camera views. In 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.

Recurrent Deep Attention Network for Person Re-Identification

PS T1.6: AI and Deep Neural Networks

poster

ICPR 2020 is the premier international conference on Pattern Recognition, gathering leading researchers from around the world to present their latest achievements in Pattern Analysis and Recognition, applied Artificial Intelligence and Machine Learning. The topic of the year is " Putting Artificial intelligence to work on Patterns". 
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Changhao Wang

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Recurrent Deep Attention Network for Person Re-Identification

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