Tracking multiple objects in traffic scenarios using free-form obstacle delimiters and particle filters

Abstract

Dynamic environment representation is an important research task in the field of advanced driving assistance systems. Usually, the tracking process is influenced by several factors, such as the unpredictable and deformable nature of the obstacles, the measurement uncertainties or the occlusions. This paper presents a stereo-vision based approach for tracking multiple objects in unstructured environments. The proposed technique relies on measurement data provided by an intermediate grid map and the object delimiters extracted from this grid. We present a particle filter based tracking solution in which a particle state is described by two components: the dynamic object parameters, and the object's geometry. In order to solve the high dimensionality state space problem a Rao-Blackwellized Particle Filter is used. The proposed method takes into consideration the stereo uncertainties and relies on a weighting mechanism based on the particle alignment error.