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Multi-camera Pedestrian Detection by Means of Track-To-Track Fusion and Car2Car Communication

In this paper a system for fusion of pedestrian detections from multiple vehicles is presented. The application area is narrowed down to driver assistance systems, where single cameras are mounted in the moving vehicles. The main contribution of this paper is a comparison of three fusion algorithms based on real image data. The methods under review include Covariance Fusion, Covariance Intersection, and Covariance Union. An experimental setup is presented, with known ground truth positions of the detected objects. This information can be incorporated for the evaluation of the fusion methods. The system setup consists of two vehicles equipped with LANCOM R wireless access points, cameras, inertial measurement units (IMU) and IMU enhanced GPS receivers. Each vehicle detects pedestrians by means of the camera and an AdaBoost detection algorithm. The results are tracked and transmitted to the other vehicle in appropriate coordinates. Afterwards each vehicle is responsible for reasonable treatment or fusion of the detection data.