Tracking cars in range images using the CONDENSATION algorithm
Esther Meier, Frank Ade · 2003
The detection of objects in every frame of a sequence is often not sufficient for scene interpretation. Tracking can increase the robustness, especially when occlusions occur or when objects temporarily disappear. In this paper we present a stochastic tracking approach which is based on the CONDENSATION algorithm (conditional density propagation over time) that is capable of tracking multiple objects with multiple hypotheses in range images. A probability density function describing the likely state of the objects is propagated over time using a dynamic model. The measurements influence the probability function and allow the incorporation of new objects into the tracking scheme. Additionally, the representation of the density function with a fixed number of samples ensures a constant running time per iteration step. Results with data from different sources are shown for automotive applications.