Tracking ground moving extended objects using RGBD data

Abstract

This paper is about an experimental set-up for tracking a ground moving mobile object from a bird's eye view. In this experiment, an RGB and depth camera is used for detecting moving points. The detected points serve as input for a probabilistic extended object tracking algorithm that simultaneously estimates the kinematic parameters and the shape parameters of the object. By this means, it is easy to discriminate moving objects from the background and the probabilistic tracking algorithm ensures a robust and smooth shape estimate. We provide an experimental evaluation of a recent Bayesian extended object tracking algorithm based on a so-called Random Hypersurface Model and give a comparison with active contour models.