Human feet tracking guided by locomotion model

Abstract

Following a person is a fundamental requirement for human-robot interaction. In this paper we propose a novel tracking approach for robust human feet tracking which integrates human locomotion into tracking algorithms. The vertical displacement between the two feet is analyzed and we observe that this displacement during the walking cycle is close to a modulated cosine waveform. Based on this, we propose an adaptive model for the human walking pattern. We divide the motion of the human feet into local motion and global motion. The local motion is modeled by a modified cosine wave that updates along time. Global motion is estimated by the continuity between successive frames. This model is combined with particle filtering to guide the searching of the feet. A 2D Gaussian mask is generated according to the predicted position estimated by the motion model and used to modify the weight of the particles. Experiments are implemented in several human walking videos and the algorithm is evaluated against the generic particle filtering method. Results show that the feet can be tracked successfully with significant improvements compared to the generic particle filtering method.