Body joint tracking in low resolution video using region-based filtering

Abstract

We propose a region-based body joint tracking scheme to track and estimate continuous joint locations in low resolution imagery where the estimated trajectories can be analyzed for specific gait signatures. The true transition between the joint states are of a continuous nature and specifically follows a sinusoidal trajectory. Recent state of art techniques enables us to estimate pose at each frame from which joint locations can be deduced. But these pose estimates at low resolution are often noisy and discrete and hence not suitable for further gait analysis. Our proposed 2-level region-based tracking scheme gets a good approximation to the true trajectory and obtains finer estimates. Initial joint locations are deduced from a human pose estimation algorithm and subsequent finer locations are estimated and tracked by a Kalman filter. We test the algorithm on sequences containing individuals walking outdoors and evaluate their gait using the estimated joint trajectories.