Abstract
In this study, a Man-machine Finger-guessing game is designed based on the IntelliSense and Man-machine coordination mechanism of hand gesture. The image sequence is obtained by the Kinect and the human hand is extracted using segmentation and skin color modeling. The proposed SCDDF (Shape Context Density Distribution Feature), which combined DDF (Density Distribution Feature) algorithm and shape context recognition algorithm, is used to extract gesture identity. Gestures are finally identified by registering with templates in the pre-established gesture library. Furthermore, we proposed a new human-computer cooperative mechanism, including two points: (1) The virtual interface is used to control the ‘Midas Touch problem’. (2) The whole game is more natural and smooth. In the aspect of gesture recognition, we combined DDF algorithm and shape context recognition algorithm, and proposed the SCDDF algorithm. The new algorithm improved recognition rate by 14.3% compared with DDF algorithm.
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References
Sykora, P., Kamencay, P., Hudec, R.: Comparison of SIFT and SURF Methods for Use on Hand Gesture Recognition based on Depth Map. AASRI Procedia 9, 19–24 (2014)
Yanyan, C., Zhengming, C., Xiaoqin, Z.: Gesture recognition based on Kinect and its application in virtual assembly technology. Electron. Des. Eng. 21(10), 4–7 (2013)
Jie, Z.: A new generation of game revolution-a small discussion Kinect somatosensory game. Popular Lit. 22, 300 (2011)
Jesús Luis-González-Ibánez, J., Wang, A.I.: Learning recycling from playing a Kinect game. Int. J. Game-Based Learn. (IJGBL) 5(3), 25–44 (2015)
Boutsika, E.: Kinect in education: a proposal for children with autism. Procedia Comput. Sci. 27, 123–129 (2014)
Lin, J., Sun, Q., Li, G., He, Y.: SnapBlocks: a snapping interface for assembling toy blocks with XBOX Kinect. Multimedia Tools Appl. 73(3), 2009–2032 (2014)
Zhang, L., Haibo, T., Xiaofeng, L., He, Z.: Application of Kinect motion capture technology in health care. J. Comput. Technol. Dev. 08, 104–108 (2016)
Zhu, Y., Wang, X., Tang, W., Wu, T.: Three somatosensory interaction of virtual mouse based on Kinect
Wang, S.B., Quattoni, A., Morency, L., et al.: Hidden conditional random fields for gesture recognition. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1521–1527. IEEE (2006)
Guo, X., Ge, Y., Wang, L.: Classification and recognition algorithm of alphabet gesture based on shape feature. Comput. Eng. 18, 130–132 (2004)
Haitham, H., Abdul-Kareem, S.: Static hand gesture recognition using neural networks. Artif. Intell. Rev. 1–35 (2012)
Xu, Y.: Kinematic Sensor Based on the Dynamic Gesture Recognition. Southwest University (2014)
Mihailescu, M., Teo, Y.M.: Dynamic resource pricing on federated clouds. In: 2010 10th IEEE ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), pp. 513–517. IEEE (2010)
Yan, W., Qizhi, Z.: Gesture recognition based on Kinect depth information. J. Beijing Inf. Sci. Technol. Univ. 28(1), 22–26 (2013)
Jipu, T., Wensheng, X.: Fingerprint detection and gesture recognition based on Kinect. J. Comput. Appl. 06, 1795–1800 (2015)
Shilin, Z., Bo, Z.: Using HMM to sign language video retrieval. In: Proceedings of the 2nd International Conference on Computational Intelligence and Natural Computing, pp. 55–59. IEEE Computer Society, Washington D.C. (2010)
Xingcheng, P., Tao, W., Yi, Z.: Kinect gesture recognition based on modified Hu moment algorithm. Comput. Eng. 07, 165–172 + 180 (2016)
Hui, L., Zhiquan, F., Liwei, L., Zhipeng, X.: (Natural Science Edition) Journal of Zhejiang University (Engineering Science), 06, 1167–1175 (2016)
Fan, M.: Research on real-time gesture tracking algorithm based on cognitive behavior model library and Kinect platform. Jinan University (2014)
Rong, L., Ming, L.: Gesture recognition based on triaxial acceleration sensor. Comput. Eng. 37(24), 141–143 (2011)
Fang, Y.-K., Cheng, J., Wang, K.-Q., Lu, H.-Q.: Geometry recognition based on fast-scale spatial feature detection. J. Image Graph. 14(2), 214–220 (2009)
Lee, H.K., Kim, J.H.: An HMM-based threshold model approach for gesture recognition. IEEE Trans. Pattern Anal. Mach. Intell. 21(10), 961–973 (1999)
Zhou, X., Feng, Z., Qiao, Y., et al.: The design of man-machine finger-guessing game based on the hand gesture of the IntelliSense. In: 2016 International Conference on Cyberworlds (CW), pp. 97–103. IEEE (2016)
Fan, W., Chen, X., Yang, J.: Analog gesture recognition based on improved shape context descriptor. Electron. Technol. 07, 1–4 (2010)
Gu, J., Chen, X., Yang, J.: Application of shape context descriptor based on adaptive template radius in gesture recognition. Biomed Biomed. Eng. 05, 463–468 + 485 (2009)
Acknowledgments
This paper is supported by the National Natural Science Foundation of China (No. 61472163 and No. 61603151), partially supported by the National Key Research & Development Plan of China (No. 2016YFB1001403), the Science and technology project of Shandong Province (No. 2015GGX101025).
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Zhou, X., Feng, Z., Qiao, Y., Fan, X., Yang, X. (2017). The Man-Machine Finger-Guessing Game Based on Cooperation Mechanism. In: L. Gavrilova, M., Tan, C., Sourin, A. (eds) Transactions on Computational Science XXX. Lecture Notes in Computer Science(), vol 10560. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-56006-8_6
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DOI: https://doi.org/10.1007/978-3-662-56006-8_6
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