Abstract
This paper demonstrates a gesture recognition approach based on binocular camera. The binocular vision system can deal with stereo imaging problem using disparity map. After the cameras are calibrated, the approach uses skin color model and depth information to separate the hand from the environment in the image. And the features of the gestures are extracted by feature extraction algorithm. These gestures as well as their features constitute a set of training examples in machine learning. The Support Vector Machine (SVM), which is supervised learning models, are used to classify these gestures that are labeled with their meaning, such as digits gesture. In training and classification processes, we use the same feature extraction algorithm handling the gesture image and SVM can recognize the meaning of a gesture. The gesture recognition method mentioned in this paper represents a high accuracy in recognizing number gestures.
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Acknowledgment
This research work is supported by Guangdong province science and technology plan projects (2015A020219001, 2017A010101031). The Fundamental Research Funds for the Central Universities (2015ZM140, 2017MS048). Guangzhou Key Laboratory of Robotics and Intelligent Software (15180007). Shenzhen peacock project (KQTD20140630154026047). Shenzhen basic research projects (JCYJ20160429161539298). Guangdong Ministry of Education Foundation (2013B090500093).
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Feng, L., Bi, S., Dong, M., Liu, Y. (2017). A Gesture Recognition Method Based on Binocular Vision System. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_23
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DOI: https://doi.org/10.1007/978-3-319-68345-4_23
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