Detecting Hands in Video Images using Scale Invariant Local Descriptors

J. Richarz, T. Pltz, and G.A. Fink (Germany)


hand detection, humancomputer interaction, scale invariant features


In this paper, we describe our approach on hand detection in cluttered images using scale invariant features. We claim that, while modelling hands as a whole is bound to fail because of their strongly articulated nature, treating them as a collection of weakly connected characteristic regions seems promising. Different approaches to ļ¬nding and ro bustly modelling such regions or local object descriptors invariantly to scale and orientation of the object in ques tion have been proposed. As an example, we demonstrate our approach using the well-known scale-invariant feature transform (SIFT), combined with a region-based postpro cessing to eliminate false positives. We present detailed results on a large set of images from a realistic interaction scenario with a smart room.

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