Improved Region-Based Kalman Filter For Tracking Body Joints And Evaluating Gait In Surveillance Videos

We propose an Improved Region-based Kalman filter to estimate fine precise body joint trajectories to facilitate gait analysis from low resolution surveillance cameras. This is important because existing pose estimation and tracking techniques obtain noisy and discrete trajectories which are insufficient for gait analysis. Our objective is to obtain a close approximation to the true sinusoidal/non-linear transition of the body joint locations between consecutive time instants. The proposed filter models the non-linear transitions along the sinusoidal trajectory, and incorporates a refining technique to determine the fine precision estimates of the body joint location using prior information from the individual's rough pose. The proposed technique is evaluated on an outdoor low-resolution gait dataset categorized by individuals wearing a weighted vest (simulating a threat) or no weighted vest. Experimental results and comparisons with similar representative methods prove the accuracy and precision of the proposed filter for fine-precision body joint tracking. With respect to analyzing gait for threat identification, the proposed scheme exhibits better accuracy than state of the art pose discrete estimates.