Deep Reinforcement Learning-Based DQN Agent Algorithm for Visual Object Tracking in a Virtual Environmental Simulation

Abstract

The complexity of object tracking models among hardware applications has become a more in-demand task to accomplish with multifunctional algorithm skills in various indeterminable environment tracking conditions. Experimenting with the virtual realistic simulator brings new dependencies and requirements, which may cause problems while experimenting with runtime processing. The goal of this paper is to present an object tracking framework that differs from the most advanced tracking models by experimenting with virtual environment simulation (Aerial Informatics and Robotics Simulation—AirSim, City Environ) using one of the Deep Reinforcement Learning Models named as Deep Q-Learning algorithms. Our proposed network examines the environment using a deep reinforcement learning model to regulate activities in the virtual simulation environment and utilizes sequential pictures from the realistic VCE (Virtual City Environ) model as inputs. Subsequently, the deep reinforcement network model was pretrained using multiple sequential training image sets and fine-tuned for adaptability during runtime tracking. The experimental results were outstanding in terms of speed and accuracy. Moreover, we were unable to identify any results that could be compared to the state-of-the-art methods that use deep network-based trackers in runtime simulation platforms, since this testing experiment was conducted on the two public datasets VisDrone2019 and OTB-100, and achieved better performance among compared conventional methods.