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Tracking Cars in Range Images Using the Condensation Algorithm

Esther B. Meier and Frank Ade
IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems


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.

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  author = {Esther B. Meier and Frank Ade},
  title = {Tracking Cars in Range Images Using the Condensation Algorithm},
  booktitle = {IEEE/IEEJ/JSAI International Conference on Intelligent Transportation Systems},
  year = {1999},
  pages = {129-134},
  keywords = {range data, tracking, Condensation algorithm}