Hand gesture recognition in complicate scenario is still a challenging problem in computer vision domain. In this paper, a novel hand gesture recognition system is presented. To detect the exact hand target from complicate scenarios, the color and motion clues are used to obtain potential hand regions. And then a method named Motion Times Image (MTI) is proposed to identify the optimal hand location. The R-transform descriptor is used to describe the hand shape features and an offline trained Support Vector Machine with Radial Basis Function kernels (RBF-SVM) is exploited to perform the hand gesture recognition task. Extensive experiments with different users under dynamic and complicate scenarios are conducted to show its high recognition accuracy and strong robustness. © 2010 Springer-Verlag.
CITATION STYLE
Song, Z., Yang, H., Zhao, Y., & Zheng, F. (2010). Hand detection and gesture recognition exploit motion times image in complicate scenarios. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6454 LNCS, pp. 628–636). https://doi.org/10.1007/978-3-642-17274-8_61
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