Road user tracking using a Dempster-Shafer based classifying multiple-model PHD filter

Multi-object tracking requires appropriate motion models to predict the objects´ states. In case of road user tracking, objects with different motion characteristics have to be concerned. Moreover, the motion characteristics and with that the appropriate motion model depends on the object´s class. In this contribution a classifying multiple-model probability hypothesis density filter based on Dempster-Shafer theory is proposed. The object class is estimated based on features of the measurement as well as features of the estimated objects´ states. Furthermore, the transition probabilities between the model modes are not static, but adapted with the estimated class probabilities of each track. It is shown, that a single multiple model filter is able to track multiple road users with different motion characteristics. Additionally, the integration of the Dempster-Shafer based classification in the filter framework improves the object class estimation significantly. Finally, an application of the filter on real world data of an intersection perception system is presented.