Overview of Reinforcement Learning for Person Re-Identification

Abstract

For intelligent surveillance, the issue of person re-identification has attracted extensive research interest due to its great academic value and broad application prospect. This issue aims to build the correspondence between human appearances across deployed non-overlapping cameras. However, the captured human images are inevitably influenced and deteriorated by various factors, such as illumination variations, pose changes, viewpoint alterations, partial occlusions, and surrounding clutters, which largely barricade the progress of re-identification performance. While, owing to the development and prosperity of reinforcement learning, researchers have made a big breakthrough in this issue. Motivated by the research breakthrough, this paper provides an overview of reinforcement learning for person re-identification. The contributions of this paper can mainly be summarized as follows: (a) introducing the background and challenges of person re-identification, and describing the framework and characteristics of general reinforcement learning methodology; (b) surveying the latest and most representative researches on reinforcement learning methods for this issue, and analyzing the mechanisms and essences of these methods; (c) clarifying the advantages of reinforcement learning, discussing its potential problems, proposing the conceptual schemes around it, and thus illuminating its future trend for this issue.