Abstract
This paper presents a method for tracking multiple skin-colored objects in images acquired by a possibly moving camera. The proposed method encompasses a collection of techniques that enable the modeling and detection of skin-colored objects as well as their temporal association in image sequences. Skin-colored objects are detected with a Bayesian classifier which is bootstrapped with a small set of training data. Then, an off-line iterative training procedure is employed to refine the classifier using additional training images. On-line adaptation of skin-color probabilities is used to enable the classifier to cope with illumination changes. Tracking over time is realized through a novel technique which can handle multiple skin-colored objects. Such objects may move in complex trajectories and occlude each other in the field of view of a possibly moving camera. Moreover, the number of tracked objects may vary in time. A prototype implementation of the developed system operates on 320x240 live video in real time (28Hz) on a conventional Pentium 4 processor. Representative experimental results from the application of this prototype to image sequences are also provided.
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Argyros, A.A., Lourakis, M.I.A. (2004). Real-Time Tracking of Multiple Skin-Colored Objects with a Possibly Moving Camera. In: Pajdla, T., Matas, J. (eds) Computer Vision - ECCV 2004. ECCV 2004. Lecture Notes in Computer Science, vol 3023. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24672-5_29
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DOI: https://doi.org/10.1007/978-3-540-24672-5_29
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