Target Tracking

Target tracking involves estimating the position and trajectory of one or more objects over time, a crucial task with applications ranging from autonomous driving to surveillance. Current research emphasizes robust tracking in challenging environments (e.g., cluttered urban areas, presence of occlusions) using diverse approaches, including particle filters, Gaussian processes, deep reinforcement learning, and neural networks (e.g., transformers, convolutional neural networks). These advancements improve accuracy and reliability, particularly in scenarios with noisy data, multiple targets, and dynamic obstacles, impacting fields like robotics, autonomous systems, and sensor network applications.