Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

查看全文 作　　者：GOUTSU [1]Yusuke;KOBAYASHI [1]Takaki;OBARA [1]Junya;KUSAJIMA [1]Ikuo;TAKEICHI [1]Kazunari;TAKANO [1]Wataru;NAKAMURA [1]Yoshihiko

机构地区：[1]Department of Mechano-Informatics,School of Information Science and Technology,University of Tokyo,7-3-1 Hongo,Bunkyo-ku,Tokyo,Japan

出　　处：《Chinese Journal of Mechanical Engineering》索引2015年第28卷第4期,共9页

基　　金：Supported by Grant-in-Aid for Young Scientists(A)(Grant No.26700021);Japan Society for the Promotion of Science and Strategic Information and Communications R&D Promotion Programme(Grant No.142103011);Ministry of Internal Affairs and Communications 摘　　要：Gesture recognition is used in many practical applications such as human-robot interaction, medical rehabilitation and sign language. With increasing motion sensor development, multiple data sources have become available, which leads to the rise of multi-modal gesture recognition. Since our previous approach to gesture recognition depends on a unimodal system, it is difficult to classify similar motion patterns. In order to solve this problem, a novel approach which integrates motion, audio and video models is proposed by using dataset captured by Kinect. The proposed system can recognize observed gestures by using three models. Recognition results of three models are integrated by using the proposed framework and the output becomes the final result. The motion and audio models are learned by using Hidden Markov Model. Random Forest which is the video classifier is used to learn the video model. In the experiments to test the performances of the proposed system, the motion and audio models most suitable for gesture recognition are chosen by varying feature vectors and learning methods. Additionally, the unimodal and multi-modal models are compared with respect to recognition accuracy. All the experiments are conducted on dataset provided by the competition organizer of MMGRC, which is a workshop for Multi-Modal Gesture Recognition Challenge. The comparison results show that the multi-modal model composed of three models scores the highest recognition rate. This improvement of recognition accuracy means that the complementary relationship among three models improves the accuracy of gesture recognition. The proposed system provides the application technology to understand human actions of daily life more precisely. 关键词：运动传感器多模态模型手势识别集成模型音视频隐马尔可夫模型识别系统学习方法

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Multi-modal Gesture Recognition using Integrated Model of Motion, Audio and Video

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