Gesture Recognition by Learning Local Motion Signatures

Abstract : This paper overviews a new gesture recognition framework based on learning local motion signatures (LMSs) introduced by [5]. After the generation of these LMSs computed on one individual by tracking Histograms of Oriented Gradient (HOG) [2] descriptor, we learn a codebook of video-words (i.e. clusters of LMSs) using k-means algorithm on a learning gesture video database. Then the videowords are compacted to a codebook of code-words by the Maximization of Mutual Information (MMI) algorithm. At the final step, we compare the LMSs generated for a new gesture w.r.t. the learned codebook via the k-nearest neighbors (k-NN) algorithm and a novel voting strategy. Our main contribution is the handling of the N to N mapping between code-words and gesture labels with the proposed voting strategy. Experiments have been carried out on two public gesture databases: KTH [16] and IXMAS [19]. Results show that the proposed method outperforms recent state-of-the-art methods.
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Communication dans un congrès
CVPR 2010 : IEEE Conference on Computer Vision and Pattern Recognition, Jun 2010, San Franscico, CA, United States. IEEE Computer Society Press, 2010
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Dernière modification le : samedi 27 janvier 2018 - 01:30:47
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Mohamed Kaâniche, François Bremond. Gesture Recognition by Learning Local Motion Signatures. CVPR 2010 : IEEE Conference on Computer Vision and Pattern Recognition, Jun 2010, San Franscico, CA, United States. IEEE Computer Society Press, 2010. 〈inria-00486110〉

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