  Pedestrian Attribute Recognition (PAR) is a challenging task in intelligent
video surveillance. Two key challenges in PAR include complex alignment
relations between images and attributes, and imbalanced data distribution.
Existing approaches usually formulate PAR as a recognition task. Different from
them, this paper addresses it as a decision-making task via a reinforcement
learning framework. Specifically, PAR is formulated as a Markov decision
process (MDP) by designing ingenious states, action space, reward function and
state transition. To alleviate the inter-attribute imbalance problem, we apply
an Attribute Grouping Strategy (AGS) by dividing all attributes into subgroups
according to their region and category information. Then we employ an agent to
recognize each group of attributes, which is trained with Deep Q-learning
algorithm. We also propose a Group Optimization Reward (GOR) function to
alleviate the intra-attribute imbalance problem. Experimental results on the
three benchmark datasets of PETA, RAP and PA100K illustrate the effectiveness
and competitiveness of the proposed approach and demonstrate that the
application of reinforcement learning to PAR is a valuable research direction.
