Pedestrian Detection and Tracking Using a Dynamic Vision Sensor

Abstract

Neuromorphic sensors such as the DynamicVision Sensor (DVS) emulate the behavior of the primary vision system. Its asynchronous behavior makes the data processing easier and faster due to the analysis is only in the active pixels. Pedestrian kinematics contains specific movement patterns feasible to be detected, like the angular movement of arms and feet. Some previous methodologies were focused on pedestrian detection based on the static shapes detection like cylinders orcircles, however, they do not take into account the kinematic behavior of the body by itself. In this paper, we presented an algorithm inspired in K-means clustering and describes the analysis of the human kinematics based on DVS in order to detect and track pedestrians in a controlled environment.