Abstract
For the problem of low manual detection accuracy under the conditions of illumination and occlusion, the detection of human hands based on common optical images was explored, and an accurate manual detection method under general conditions was proposed. The method based on skin color model combined with Convolutional Neural Network (CNN) was mainly used. Realize the detection of human hands. Firstly, the skin color model is obtained according to the characteristics of skin color in the HSV (Hue, Saturation and Value) space, which is used to segment skin area. On this basis, a convolutional neural network for the detection of human hand contours is constructed, which is used to extract the human hand contour features to constrain skin region to obtain the hand region. The results show that even in light and shielding, it also has adaptability, which improves the accuracy of hand detection.
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Acknowledgements
This research work is supported by the grant of Guangxi science and technology development project (No: AB17195053), the grant of Guangxi Science Foundation (No: 2017GXNSFAA198226), the grant of Guangxi Key Laboratory of Cryptography & Information Security of Guilin University of Electronic Technology (No: GCIS201604), the grant of Guangxi Colleges and Universities Key Laboratory of Intelligent Processing of Computer Images and Graphics of Guilin University of Electronic Technology (No: GIIP201602), and the grant of Innovation Project of GUET Graduate Education(2017YJCX55).
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Pan, H. et al. (2018). Accurate Hand Detection Method for Noisy Environments. In: Sun, X., Pan, Z., Bertino, E. (eds) Cloud Computing and Security. ICCCS 2018. Lecture Notes in Computer Science(), vol 11068. Springer, Cham. https://doi.org/10.1007/978-3-030-00021-9_33
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DOI: https://doi.org/10.1007/978-3-030-00021-9_33
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