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.
Joint Probabilistic Data Association fusion approach for pedestrian detection
2013 IEEE Intelligent Vehicles Symposium (IV) ; 1344-1349
2013-06-01
1144874 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
JOINT PROBABILISTIC DATA ASSOCIATION FUSION APPROACH FOR PEDESTRIAN DETECTION
British Library Conference Proceedings | 2013
|Suboptimal joint probabilistic data association
IEEE | 1993
|Suboptimal Joint Probabilistic Data Association
Online Contents | 1993
|Joint integrated probabilistic data association: JIPDA
IEEE | 2004
|Improved joint probabilistic data association algorithm
IEEE | 2002
|