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Combination of HMM and DTW for 3D dynamic gesture recognition using depth only

Abstract : Gesture recognition is one of the important tasks for human Robot Interaction (HRI). This paper describes a novel system intended to recognize 3D dynamic gestures based on depth information provided by Kinect sensor. The proposed system utilizes tracking for the upper body part and combines the hidden Markov models (HMM) and dynamic time warping (DTW) to avoid gestures misclassification. By using the skeleton algorithm provided by the Kinect SDK, body is tracked and joints information are extracted. Each gesture is characterized by one of the angles which remains active when executing it. The variations of the angles throughout the gesture are used as inputs of Hidden Markov Models (HMM) in order to recognize the dynamic gestures. By feeding the output of (HMM) back to (DTW), we achieved good classification performances without any misallocation. Besides that, using depth information only makes our method robust against environmental conditions such as illumination changes and scene complexity.
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Contributor : Frédéric Davesne Connect in order to contact the contributor
Submitted on : Friday, August 5, 2016 - 3:17:55 PM
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Hajar Hiyadi, Fakhr-Eddine Ababsa, Christophe Montagne, El Houssine Bouyakhf, Fakhita Regragui. Combination of HMM and DTW for 3D dynamic gesture recognition using depth only. 12th International Conference on Informatics in Control, Automation and Robotics (ICINCO 2015), Jul 2015, Colmar, France. pp.229--245, ⟨10.1007/978-3-319-31898-1_13⟩. ⟨hal-01352115⟩



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