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Pedestrian Attribute Recognition (PAR) is a challenging task in intelligentvideo surveillance. Two key challenges in PAR include complex alignmentrelations between images and attributes, and imbalanced data distribution.Existing approaches usually formulate PAR as a recognition task. Different fromthem, this paper addresses it as a decision-making task via a reinforcementlearning framework. Specifically, PAR is formulated as a Markov decisionprocess (MDP) by designing ingenious states, action space, reward function andstate transition. To alleviate the inter-attribute imbalance problem, we applyan Attribute Grouping Strategy (AGS) by dividing all attributes into subgroupsaccording to their region and category information. Then we employ an agent torecognize each group of attributes, which is trained with Deep Q-learningalgorithm. We also propose a Group Optimization Reward (GOR) function toalleviate the intra-attribute imbalance problem. Experimental results on thethree benchmark datasets of PETA, RAP and PA100K illustrate the effectivenessand competitiveness of the proposed approach and demonstrate that theapplication of reinforcement learning to PAR is a valuable research direction.

Reinforced Pedestrian Attribute Recognition with Group Optimization Reward