Recognizing Gestures by Learning Local Motion Signatures of HOG Descriptors

Mohamed Kaâniche (1) , Francois Bremond (2)

1 MASC - Département Mathématiques Appliquées, Signaux et Communications
2 STARS - Spatio-Temporal Activity Recognition Systems

We introduce a new gesture recognition framework based on learning local motion signatures (LMSs) of HOG descriptors . 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) descriptor, we learn a code-book of video-words (i.e. clusters of LMSs) using kmeans algorithm on a learning gesture video database. Then the video-words are compacted to a code-book 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 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 code-words and gesture labels within the proposed voting strategy. Experiments have been carried out on two public gesture databases: KTH and IXMAS . Results show that the proposed method outperforms recent state-of-the-art methods

Domaines: Traitement des images [eess.IV]

Mohamed Kaâniche, Francois Bremond. Recognizing Gestures by Learning Local Motion Signatures of HOG Descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012. ⟨hal-00696371⟩