Active Object Tracking

Active object tracking (AOT) focuses on autonomously controlling a tracker's movement to maintain a desired relationship with a target, leveraging visual information. Current research emphasizes developing robust and generalizable AOT systems, particularly using deep reinforcement learning (DRL) architectures like DQN and incorporating elements such as multi-head attention and recurrent networks to improve performance in complex, dynamic environments. This field is crucial for advancing robotics, autonomous driving, and space exploration, enabling applications like autonomous rendezvous, debris removal, and improved surveillance capabilities.