3D Multi Object Tracking

3D multi-object tracking (3D MOT) aims to accurately and consistently identify and track multiple objects moving in three-dimensional space, a crucial task for applications like autonomous driving and robotics. Current research emphasizes improving tracking accuracy and robustness, particularly in challenging scenarios involving occlusions, dense environments, and diverse object motions, often employing deep learning architectures such as transformers and graph neural networks, along with advanced data association techniques and sensor fusion (e.g., camera-LiDAR). These advancements are significantly impacting fields like autonomous navigation and robotics by enabling more reliable perception and decision-making in complex dynamic environments.