Object Trajectory

Object trajectory research focuses on accurately modeling and predicting the movement of objects over time, crucial for applications like autonomous driving and robotics. Current research emphasizes robust tracking algorithms, often employing graph-based methods, deep learning architectures (like transformers and variational autoencoders), and advanced representations such as dual quaternions to handle complex scenarios including occlusions and camera motion. These advancements improve the accuracy and efficiency of object tracking, enabling better understanding of dynamic scenes and facilitating applications requiring precise spatiotemporal awareness.