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Robust Head Detection and Tracking in Cluttered Workshop Environments Using GMM

  • 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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Metadaten
Document Type:Conference Object
Language:English
Parent Title (English):Kropatsch, Sablatnig et al. (Eds.): Pattern Recognition. Proceedings of the 27th DAGM Symposium, Vienna, Austria, August 31 - September 2, 2005. Lecture Notes in Computer Science, Vol. 3663
First Page:442
Last Page:450
ISBN:978-3-540-28703-2
DOI:https://doi.org/10.1007/11550518_55
Publisher:Springer
Publication year:2005
Departments, institutes and facilities:Fachbereich Informatik
Institute of Visual Computing (IVC)
Dewey Decimal Classification (DDC):000 Informatik, Informationswissenschaft, allgemeine Werke / 000 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Entry in this database:2015/04/02