Abstract
A vision based head tracking approach is presented, combining foreground information with an elliptical head model based on the integration of gradient and skin-color information. The system has been developed to detect and robustly track a human head in cluttered workshop environments with changing illumination conditions. A foreground map based on Gaussian Mixture Models (GMM) is used to segment a person from the background and to eliminate unwanted background cues. To overcome known problems of adaptive background models, a high-level feedback module prevents regions of interest to become background over time. To obtain robust and reliable detection and tracking results, several extensions of the GMM update mechanism have been developed.
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© 2005 Springer-Verlag Berlin Heidelberg
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Barth, A., Herpers, R. (2005). Robust Head Detection and Tracking in Cluttered Workshop Environments Using GMM. In: Kropatsch, W.G., Sablatnig, R., Hanbury, A. (eds) Pattern Recognition. DAGM 2005. Lecture Notes in Computer Science, vol 3663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11550518_55
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DOI: https://doi.org/10.1007/11550518_55
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28703-2
Online ISBN: 978-3-540-31942-9
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