Active Histogram of Oriented Gradient Based Learning for Free Palm Tracking

Hand detection is a challenging research field in computer vision due tothe high freedom of hand for discrimination especially under low imaging conditions. In this paper, we mainly develop a novel feature that we called active Histogram of Oriented Gradient (aHOG) for palm detection inunconstrained grey-level images. Toovercome the limitationsof HOG, we apply local PCA, which is a feature synthesisprocedure, to original HOG feature sets. So that the output feature takesshorter description length and are less insensitive to light variations and background clusters, without much performance penalty. Then the features are combined with LBP for better palm information mining in linear SVM for classification. Besides, we use a scale partitionstrategy to achieve fast palm tracking. In our experiments, the performance is demonstrated to be very effective on the infrared palm database collected by ourselves, which involve rich inter-plane and out-of-plane rotations.

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