Motion periodicity based pedestrian detection and particle filter based pedestrian tracking using stereo vision camera

Abstract

A novel method is proposed that adapts a previously proposed LADAR based pedestrian detection and tracking technique by introducing a stereo-vision based segmentation technique for the purpose of pedestrian detection and tracking. The proposed method detects the harmonic motions of limbs and body during a typical human walk and temporally propagates the position, stride, direction and phase using a particle filter. The particle-filter uses a human limb-motion model and is able to track the walking pedestrians in a heavily occluded environment. Potential 3D point clusters belonging to arms and feet are extracted employing an adapted version of RANSAC based segmentation algorithm. A Fourier-transform based periodogram confirms the periodicity for each point-cluster representing limbs. Since RGB or intensity data from the stereo-vision input is ignored and the proposed method completely relies upon 3D data produced by the stereo-vision sensor, reliable illumination invariant pedestrian detection and tracking results are achieved using Daimler-Stereo-Pedestrian-Detection-Dataset. Further lab experiments also confirm the viability of the method within the indoor environment.