A multisensor based approach using supervised learning and particle filtering for people detection and tracking

Abstract

People detection and tracking is an interesting skill for interactive social robots. Laser range finder (LRF) and vision based approaches are the most common although both present strengths and weaknesses. In this paper, a multisensor system to detect and track people in the proximity of a mobile robot is proposed. First, a supervised learning approach is used to recognize patterns of legs in the proximity of the robot using a LRF. After this, a tracking algorithm is developed using particle filter and the observation model of legs. Second, a Kinect sensor is used to carry out people detection and tracking. This second method uses a face detector in the color image, the color of the clothes and the depth information. The strengths and weaknesses of the second proposal are also commented. In order to put together the strengths of both sensors, a third algorithm is proposed. In this third approach both laser and Kinect data are fused to detect and track people. Finally, the multisensory approach is experimentally evaluated in a real indoor environment. The multisensor system outperforms the single sensor based approaches.