Robust People Tracking Using an Adaptive Sensor Fusion between a Laser Scanner and Video Camera

Abstract

Robust detection and tracking in a smart environment have numerous valuable applications. In this paper, an adaptive sensor fusion method which automatically compensates for bias between a laser scanner and video camera is proposed for tracking multiple people. The proposed system comprises five components: blob extraction, object tracking, scan data clustering, a cluster selection, and updating the bias. Based on the position of object in an image, the proposed system determines the candidate scan region. Then, the laser scan data in the candidate region of an object is clustered into several clusters. A cluster which has maximum probability as an object is selected using a discriminant function. Finally, a horizontal bias between the laser scanner and video camera is updated based on the selected cluster information. To evaluate the performance of the proposed system, we show error analysis and two applications. The results confirm that the proposed system can be used for a real-time tracking system and interactive virtual environment.