Trajectory Label

Trajectory labeling involves assigning semantic meaning to sequences of movement data, aiming to extract patterns and insights from diverse sources like animal tracking, video surveillance, and autonomous vehicle navigation. Current research focuses on developing efficient labeling methods, including weakly supervised approaches that leverage limited annotations and advanced models like variational autoencoders and multiple instance learning to handle complex temporal dependencies within trajectories. This field is crucial for improving the accuracy and interpretability of movement analysis across various domains, enabling applications ranging from crime detection to wildlife conservation and traffic management.