SELECTIVE EXPERIENCE REPLAY IN REINFORCEMENT LEARNING FOR REIDENTIFICATION

Person reidentification is a problem of recognizing a person across non-overlapping camera views. Pose variations, illumination conditions, low resolution images, and occlusion are the main challenges encountered in reidentification. Due to the uncontrolled environment in which the videos are captured, people could appear in different poses and due to which the appearance of a person could vary significantly. The walking direction of a person can provide a good estimation of their pose. Therefore, in this paper, we propose a reidentification system which adaptively selects an appropriate distance metric based on context of walking direction using reinforcement learning. Though experiments, we show that such a dynamic strategy outperforms static strategy learned or designed offline.