Adaptive Tracking

Adaptive tracking focuses on developing systems that can robustly track targets despite variations in appearance, environment, or target motion. Current research emphasizes the use of deep learning models, particularly transformers and Siamese networks, along with techniques like online learning and dynamic template selection to improve tracking accuracy and efficiency. These advancements are crucial for applications ranging from autonomous vehicle navigation and video surveillance to robotic control and active sensing, where reliable real-time target tracking is essential. The development of efficient and robust adaptive tracking algorithms is driving progress across multiple scientific disciplines and enabling new capabilities in various technological domains.