Visual motion analysis involves extracting and interpreting movement patterns from video and sensor data to understand object trajectories, speeds, and behavioral dynamics. Techniques include optical flow, feature tracking, and machine learning to segment motion and quantify temporal changes and trends. Tracking systems fuse detection, temporal association, and state estimation to maintain identities across frames. Filtering methods such as Kalman and particle filters, combined with data association and re-identification strategies, address occlusion and drift while balancing accuracy, robustness, and processing latency.
