Vision-based tracking of multiple objects in dynamic unstructured environments using free-form obstacle delimiters

Modeling and tracking of dynamic objects is a challenging research problem in the field of driving assistance systems. Typically, the environment to be tracked is heterogeneous and unstructured. As a consequence, the tracking system must deal with measurement uncertainties, occlusions or deformable objects. In this paper we propose a real-time object tracking solution for dynamic unstructured environments. This method relies on stereo vision-based 3D information that is mapped into an intermediate digital elevation map. We apply a recursive Bayesian approach for estimating both the obstacle dynamic parameters and its geometry. In order to compute the obstacle motion we use an Iterative Closest Points-based registration technique that takes into consideration the stereo uncertainties. In our case, the object model is represented by a reference point and N delimiter landmarks. For each target we apply a Kalman filter in order to track the obstacle position and speed. In addition, the object geometry is updated by using an independent 2×2 Kalman filter for each delimiter landmark. The proposed method works in real-time and takes into consideration the stereo uncertainties.