Multiple target detection and tracking by interacting joint probabilistic data association filter and bayesian networks: Application to real data

This paper proposes an algorithm of multiple target detection and tracking on road, developed for the laserscanner data. It is based on Bayesian networks for calculating the detection probability of target used in a JPDA filter. We propose a method based on the integration of detection probability of target in the JPDA filter, in which the joint probabilities of associations are calculated for multiple target. It then solves the problem of founding one or more observation in more than one gate of target by constructing hypothesis. A Bayesian network is used to determine this probability of detection. It includes a target model and takes into account the contextual information such as the size of the target, the distance between targets and the center of the sensor characteristics (position, measurement uncertainty). The method is applied to real data provided by a scanning laser sensor with multiple targets. as well as the possibility that an object enters and leaves the scene.