A comprehensive survey on deep reinforcement learning in object tracking
Hy Nguyen, Srikanth Thudumu, Hung Du, Rajesh Vasa, Kon Mouzakis · Machine Learning with Applications · 2025
The exploration of Deep Reinforcement Learning (DRL) in Object Tracking (OT) represents an emerging paradigm and is gaining traction as an alternative to conventional CNN-based methods. DRL’s ability to integrate spatial and temporal context and learn from interactions makes it particularly suited for the sequential decision-making required in OT. The survey reviews a range of DRL-based methods for OT, systematically collating and analyzing existing research to highlight trends and challenges. It also provides an evaluation of different DRL algorithms, categorizing them based on their performance in various dynamic environments. Additionally, we analyze existing evaluation benchmarks and simulators, along with the challenges, potential solutions, and trends in DRL-based OT methods. This paper aims to bridge the fragmented literature on DRL applications in OT, providing a unified view that identifies common approaches, challenges, and potential synergies.