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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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Submitted on : Tuesday, May 25, 2010 - 10:01:14 AM
Last modification on : Friday, February 4, 2022 - 3:19:30 AM
Long-term archiving on: : Thursday, September 16, 2010 - 3:32:39 PM


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  • HAL Id : inria-00486110, version 1



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. ⟨inria-00486110⟩



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