Part based people detection using 2D range data and images

Abstract

This paper addresses the problem of people detection using 2D range data and omnidirectional vision. The range data is often used in robotics to detect person legs. First, we will develop a reliable 2D range data leg detector using the AdaBoost algorithm. Second, instead of using the leg detector to detect people, we propose a more reliable method that takes into account the spatial arrangement of the detected legs. The method is inspired by the latest results on the from the computer vision area. Finally, we show how the part-based representations can be used to combine the leg detections from the 2D range data and upper body, lower body and full body detections from omnidirectional camera. The experimental results show highly reliable people detection when the two sensors are combined. We use Haar-like features and the AdaBoost again to construct the omnicam body parts detectors.