Virtual Simulation based Visual Object Tracking via Deep Reinforcement Learning
Khurshedjon Farkhodov, Jin‐Hyeok Park, Suk‐Hwan Lee, Ki‐Ryong Kwon · 2022
The current research field of object tracking has become noticeably popular among researchers where AI techniques take place with high-level accuracy. An algorithm with multifunctional abilities had proposed in different proposals in recent years. We proposed a tracking technique integrated with a virtual reality simulator – the AirSim (Areal Informatics and Robotics Simulation) City Environ model using one of the DRL models to control with a drone agent to examine a realistic environment. Additionally, the suggested method had tested via the two public: VisDrone2019 and OTB-100 datasets to compare with conventional strategies to show better performance among recent works.