Combining Detectors for Human Hand Detection

In this paper we present a hand detector system used for the Person Layout competition at PASCAL VOC Challenge 2010, in conjunction with an external head detector module. HOG features are extracted from the training set, and a clustering is performed in order to categorize the different poses that hands can have. A cascade of classifiers is trained for each one of the discovered hand subclasses, and a sliding-window approach is used for the detection process, followed by a filtering step. Results are shown on the corresponding Person Layout dataset from PASCAL VOC 2010.

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