A Discrete Hidden Markov Models Recognition Module for Temporal Series: Application to Real-Time 3D Hand Gestures.

Yannick Dennemont (1) , Guillaume Bouyer (1) , Samir Otmane (1) , Malik Mallem (1)

1 IBISC - Informatique, Biologie Intégrative et Systèmes Complexes

This work studies, implements and evaluates a gestures recognition module based on discrete Hidden Markov Models. The module is implemented on Matlab and used from Virtools. It can be used with different inputs therefore serves different recognition purposes. We focus on the 3D positions, our devices common information, as inputs for gesture recognition. Experiments are realized with an infra-red tracked flystick. Finally, the recognition rate is more than 90% with a personalized learning base. Otherwise, the results are beyond 70%, for an evaluation of 8 users on a real time mini-game. The rates are basically 80% for simple gestures and 60% for complex ones.

Keywords: gesture recognition; virtual reality; temporal series classification; 3D position; context-awareness

Domains: Computer Science [cs]; Human-Computer Interaction [cs.HC]

Yannick Dennemont, Guillaume Bouyer, Samir Otmane, Malik Mallem. A Discrete Hidden Markov Models Recognition Module for Temporal Series: Application to Real-Time 3D Hand Gestures.. 3rd International Conference on Image Processing Theory, Tools and Applications (IPTA 2012), Oct 2012, Istanbul, Turkey. pp.299--304, ⟨10.1109/IPTA.2012.6469509⟩. ⟨hal-00749097⟩