The use of reinforcement learning algorithms in object tracking: A systematic literature review

Abstract

Object tracking is a computer vision task that aims to locate and continuously follow the movement of an object in video frames, given an initial annotation. Despite its importance, this task can prove to be challenging due to factors such as occlusion, deformations, and fast motion. Reinforcement Learning (RL) has been proposed as a viable solution for addressing these challenges by adapting to changes in object appearance and effectively handling occlusions, which can improve system performance.This study carries out a Systematic Literature Review on the use of Reinforcement Learning in object tracking between 2015 and 2023, by collecting and analyzing current trends, metrics, and benchmarks used in the field. Guidelines proposed by Kitchenham were used to conduct the research, resulting in 75 studies being accepted based on their score on the quality scale attributed by the authors of this review. The studies were categorized to present the current state of research based on metadata, trends for publication, RL approach, RL algorithm, Deep Learning use, object tracking type, and camera control. Additionally, an analysis was performed on the evaluation process for system performance, focusing on benchmarks and metrics for Single Object Tracking, Multiple Object Tracking, and Active Object Tracking. This study addresses a gap by conducting a comprehensive Systematic Literature Review focusing exclusively on Reinforcement Learning for Object Tracking. The review offers researchers an updated, detailed, and objective scientific overview of the field that can be incorporated into future studies.