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.
Tracking cars in range images using the CONDENSATION algorithm
01.01.1999
631475 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Tracking Cars in Range Images Using the Condensation Algorithm
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