3D data driven prediction for active contour models based on geometric bounding volumes

Abstract Active contour models have proven to be a promising approach for data driven object tracking without knowledge about the problem domain and the object. Problems arise in case of fast moving objects and in natural scenes with heterogeneous background. In these cases, a prediction step is an essential part of the tracking mechanism. In this paper we describe a new approach for modelling the contour of moving objects in the 3D world. The key point is the description of moving objects by a simplified geometric model, the so-called bounding volume. The contour of moving objects is predicted by estimating the movement and the shape of the bounding volume in the 3D world and by projecting its contour to the image plane. Stochastic optimization algorithms are used to estimate shape parameters of the bounding volume. The 2D contour of the bounding volume is used to initialize the active contour, which then extracts the contour of the moving object. Thus, an update of the motion and model parameters of the bounding volume is possible. No task specific knowledge and no a priori knowledge about the object is necessary. We will show that in the case of convex polyhedral bounding volumes, this method can be applied to real-time closed-loop object tracking on standard Unix workstations. Furthermore, we present experiments which prove that the robustness for tracking moving objects in front of a heterogeneous background can be improved.