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2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Cluster tracking with Time-of-Flight cameras
Anchorage, AK, USA
June 23-June 28
ISBN: 978-1-4244-2339-2
Dan Witzner Hansen, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Mads Syska Hansen, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Martin Kirschmeyer, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Rasmus Larsen, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Davide Silvestre, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
Davide Silvestre, Informatics and Mathematical Modelling, Technical University of Denmark, Denmark
We describe a method for tracking people using a Time-of-Flight camera and apply the method for persistent authentication in a smart-environment. A background model is built by fusing information from intensity and depth images. While a geometric constraint is employed to improve pixel cluster coherence and reducing the influence of noise, the EM algorithm (Expectation Maximization) is used for tracking moving clusters of pixels significantly different from the background model. Each cluster is defined through a statistical model of points on the ground plane. We show the benefits of the Time-of-Flight principles for people tracking but also their current limitations.
Citation:
Dan Witzner Hansen, Mads Syska Hansen, Martin Kirschmeyer, Rasmus Larsen, Davide Silvestre, Davide Silvestre, "Cluster tracking with Time-of-Flight cameras," cvprw, pp.1-6, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2008
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