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RARE: people detection in crowded passages by range image reconstruction

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In this paper, we address the problem of people detection and tracking in crowded scenes using range cameras. We propose a new method for people detection and localisation based on the combination of background modelling and template matching. The method uses an adaptive background model in the range domain to characterise the scene without people. Then a 3D template is placed in possible people locations by projecting it in the background to reconstruct a range image that is most similar to the observed range image. We tested the method on a challenging outdoor dataset and compared it to two methods that each shares one characteristic with the proposed method: a similar template-based method that works in 2D and a well-known baseline method that works in the range domain. Our method performs significantly better, does not deteriorate in crowded environments and runs in real time.

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The research reported in this article was supported by SIA—Stichting Innovatie Alliantie with funding from the Dutch Ministry of Education, Culture and Science (OCW), in the framework of the Balance-IT project. This publication was supported by the Dutch national programme COMMIT in the ‘Virtual worlds for well-being’ project.

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Correspondence to Tim van Oosterhout.

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van Oosterhout, T., Englebienne, G. & Kröse, B. RARE: people detection in crowded passages by range image reconstruction. Machine Vision and Applications 26, 561–573 (2015).

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