Joint Probabilistic Data Association fusion approach for pedestrian detection

Abstract

Fusion is becoming a classic topic in Intelligent Transport System (ITS)  society. The lack of trustworthy sensors requires the combination of  several devices to provide reliable detections. In this paper a novel  approach, that takes advantage of the Joint Probabilistic Data  Association technique (JPDA) for data association, is presented. The  approach uses one of the most powerful techniques of Multiple Target  Tracking theory and adapts it to fulfill the strong requirements of road  safety applications. The different test performed proved that a  powerful association technique can enhance the capacity of Advance  Driver Assistance Systems. Two main sensors are used for pedestrian  detection: laser scanner and computer vision. Furthermore, the approach  takes advantage of the availability of other information sources i.e.  context information and online information (GPS). The detections are  fused using JPDA, enhancing the capacities of classical pedestrian  detection systems, mainly based in visual information. The test  performed also showed that JPDA improved the results offered by other  data association techniques, e.g. Global Nearest Neighbors. This work was supported by the Spanish Government through the Cicyt projects (GRANT TRA2010-20225-C03- 01) and (GRANT TRA2011-29454-C03-02). CAM through SEGAUTO-II ( S2009/DPI-1509) .