Optimizing Deep Reinforcement Learning Configurations for Single Object Tracking

Deep Reinforcement Learning (DRL) has become a critical approach for Object Tracking (OT) due to its ability to handle the sequential decision-making processes inherent in tracking tasks. By iteratively refining predictions and adapting to changes in object appearance or motion, DRL-based methods offer robust performance in complex tracking scenarios. However, most existing DRL-based OT methods have primarily focused on algorithmic framework design, often overlooking the optimization of configurations, such as action space, state space, reward function, and DRL algorithm. This oversight is significant, as optimal configuration choices can enhance DRL framework performance by up to 64%. Addressing this gap, our study investigates the impact of various configuration setups on the performance of DRL-based systems, specifically for Single Object Tracking (SOT) with a fixed camera view. Through theoretical analyses and experimentation, we demonstrate that appropriate configurations can improve tracking precision and system adaptability. The insights from this study provide a foundational guide for optimizing DRL applications in SOT with a fixed camera view, paving the way for more robust and efficient implementations in practical scenarios.