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Recognizing Gestures by Learning Local Motion Signatures of HOG Descriptors

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2 Author(s)
Kaâniche, M. ; Higher Sch. of Commun. of Tunis (Sup'Com), Univ. of Carthage, El Ghazala, Tunisia ; Brémond, F.

We introduce a new gesture recognition framework based on learning local motion signatures (LMSs) of HOG descriptors introduced by [1]. Our main contribution is to propose a new probabilistic learning-classification scheme based on a reliable tracking of local features. 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 video-words are compacted to a code-book of codewords 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 code-book 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 codewords and gesture labels within the proposed voting strategy. Experiments have been carried out on two public gesture databases: KTH [3] and IXMAS [4]. Results show that the proposed method outperforms recent state-of-the-art methods.

Published in:

Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:34 ,  Issue: 11 )

Date of Publication:

Nov. 2012

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