Real-time detection and tracking of pedestrians at intersections using a network of laserscanners

Abstract

Accident analysis shows that the majority of accidents with body injuries occur in urban areas and more than 50 percent of those urban accidents happen at intersections. Due to that a major aim of the Ko-PER project, which is part of research initiative Ko-FAS, is to improve safety at intersections by infrastructure based perception. To recognize and track the moving objects, a network of laserscanner sensors observes the intersection and provides a 3D profile of the current scene. By means of the 3D measurements a robust and adaptive Gaussian mixture background model is trained to segment the measurements of dynamic objects and static objects. After the segmentation, the foreground points of each sensor are clustered based on the density of the point clouds and finally pedestrians are classified using dimension features. This paper focuses on tracking of pedestrians, which are the most vulnerable road users. In order to be able to integrate dependencies between the states of the pedestrians, a random finite set particle filter is used to track the pedestrians. The performance of the laserscanner based tracking system is shown and evaluated with measurements from the Ko-PER test intersection at Conti-Safety-Park. Therefore, the optimal subpattern assignment (OSPA) metric is used to evaluate the object recognition and tracking system.