Tracking Paradigm

Tracking paradigms encompass methods for estimating the trajectory of objects across image sequences or point clouds, a crucial task in fields like autonomous driving and robotics. Current research heavily focuses on improving the accuracy and efficiency of 3D object tracking, particularly using deep learning models such as Siamese networks and transformers, often incorporating multi-sweep data or sequence information for robustness. These advancements aim to address limitations in existing approaches, such as generalizability across datasets and handling of challenging scenarios like sparse data or fast motion, ultimately leading to more reliable and robust object tracking in real-world applications.