Gesture Recognition by Learning Local Motion Signatures

This paper overviews a new gesture recognition framework based on learning local motion signatures (LMSs) introduced by [1]. After the generation of these LMSs computed on one individual by tracking Histograms of Oriented Gradient (HOG) [3] 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 [15] and IXMAS [18]. Results show that the proposed method outperforms recent state-of-the-art methods.

• Type: Conference
• Year: 2010
• Where: CVPR
• Authors: Mohamed-Becha Kaaniche, Francois Bremond
• Keywords: Computer Vision, CVPR 2010