Gesture recognition for robot teleoperation - Archive ouverte HAL
Communication Dans Un Congrès Année : 2016

Gesture recognition for robot teleoperation

Résumé

Interactive robotics is a vast and expanding research field. Interactions must be sufficiently natural, with robots having socially acceptable behavior by humans, adaptable to user expectations, thus allowing easy integration in our daily lives in various fields (science, industry, domestic, health : : : ). To make such interaction we choose gestures as a way of communication. Human gestures are certainly natural and flexible. In this context we developed a robust upper body gesture recognition system in order to teleoperate in the future a humanoid robot. Gestures are performed by Kinect camera for skeleton detection and tracking. A robust descriptor vector is chosen to describe gestures, named BSM feature vector which can be represent three important aspects, The connexion between different Body parts, the Shape changing during gesture and describe gesture Motion in the space. Three best-known and successful learning methods used for training and gesture classification, Random forest classification, Support vector machine and Multi layer Perceptron. The proposed method has been evaluated on two public benchmarks, the Microsoft Research Cambrige (MSRC- 12), and MSR Action3D datasets. The results obtained showed that the proposed recognition system is more relevant than the traditional methods.
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Dates et versions

hal-01544859 , version 1 (22-06-2017)
hal-01544859 , version 2 (12-09-2017)

Identifiants

  • HAL Id : hal-01544859 , version 1

Citer

Insaf Ajili, Malik Mallem, Jean-Yves Didier. Gesture recognition for robot teleoperation. 11ème journées de l'AFRV, Oct 2016, Brest, France. ⟨hal-01544859v1⟩
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