Neural networks in human motion tracking - An experimental study

A novel application of neural networks is proposed for tracking the motion of a walking pedestrian. First, the motion is summarized by trajectories consisting of sequences of state vectors, each vector defining a 2D shape contour as well as the position of the pedestrian in image coordinates. Next, the task of tracking the motion is conducted in the context of multivariate time series prediction on the motion trajectories, in which neural networks are employed to model and generalize the spatial-temporal variations underlying the motion. The experiments have shown that the proposed system is capable of tracking eight typical movements of a walking pedestrian, even though the motion sequences are highly noisy and each motion trajectory is very short in time.