Abstract
The method of gesture recognition useful in construction of hand controlled user interfaces is proposed in the paper. To validate the method, a database containing seven selected gestures, performed by six subjects is collected. In acquisition Microsoft Kinect device was used. In the first stage of introduced method, linear dimensionality reduction in respect to depth images of human silhouettes is carried out. Furthermore, the proper recognition is based on Dynamic Time Warping technique. To assess general features of human gestures across subjects, different strategies of dividing captured database into training and testing sets are taken into consideration. The obtained classification results are satisfactory. The proposed method allows to recognized gestures with almost 100% precision in case of training set containing data of classified subjects and with 75% accuracy otherwise.
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References
Bautista, M.Á., Hernández-Vela, A., Ponce, V., Perez-Sala, X., Baró, X., Pujol, O., Angulo, C., Escalera, S.: Probability-based dynamic time warping for gesture recognition on RGB-D data. In: Jiang, X., Bellon, O.R.P., Goldgof, D., Oishi, T. (eds.) WDIA 2012. LNCS, vol. 7854, pp. 126–135. Springer, Heidelberg (2013)
Bhattacharya, S., Czejdo, B., Perez, N.: Gesture classification with machine learning using kinect sensor data. In: Third International Conference on Emerging Applications of Information Technology, Kolkata, pp. 348–351 (2012)
Biwas, K.K., Basu, S.K.: Gesture recognition using microsoft kinect. In: 5th International Conference on Automation, Robotics and Applications, Wellington, pp. 100–103 (2011)
Doliotis, P., Stefan, A., McMurrough, C., Eckhard, D., Athitsos, V.: Comparing gesture recognition accuracy using color and depth information. In: Betke, M., Maglogiannis, I., Pantziou, G.E. (eds.) PETRA, p. 20. ACM (2011)
Gu, Y., Do, H., Ou, Y., Sheng, W.: Human gesture recognition through a kinect sensor. In: IEEE International Conference on Robotics and Biometrics, Guangzhou, pp. 1379–1385 (2012)
Mitra, S., Acharya, T.: Gesture recognition: A survey. IEEE Transactions on Systems, Man, and Cybernetics - part C: Applications and Reviews 37, 311–324 (2007)
Patsadu, O., Nukoolkit, C., Watanapa, B.: Human gesture recognition using kinect camera. In: Ninth International Joint Conference on Computer Science and Software Engineering (JCSSE), Bangkok, pp. 28–32 (2012)
Ren, Z., Meng, J., Yuan, J., Zhang, Z.: Robust hand gesture recognition with kinect sensor. In: 19th ACM International Conference on Multimedia, New York, pp. 759–760 (2011)
Sakoe, H., Chuba, S.: Dynamic Programming Algorithm Optimization for Spoken Word Recognition. IEEE Transactions on Acoustics, Speech and Signal Processing 8, 43–49 (1978)
Switonski, A., Joinski, H., Zghidi, H., Wojciechowski, K.: Selection of pose configuration parameters of motion capture data based on dynamic time warping. In: 12th International Conference of Numerical Analysis and Applied Mathematics, Rodos (2014)
Świtoński, A., Josiński, H., Jędrasiak, K., Polański, A., Wojciechowski, K.: Classification of poses and movement phases. In: Bolc, L., Tadeusiewicz, R., Chmielewski, L.J., Wojciechowski, K. (eds.) ICCVG 2010, Part I. LNCS, vol. 6374, pp. 193–200. Springer, Heidelberg (2010)
Switonski, A., Joisski, H., Jedrasiak, K., Polanski, A., Wojciechowski, K.: Diagnosis of the motion pathologies based on a reduced kinematical data of a gait. Electrical Review 57, 173–176 (2011)
Wang, Y., Yang, C., Wu, X., Xu, S., Li, H.: Kinect based dynamic hand gesture recognition algorithm research. In: 4th International Conference on Intelligent Human-Machine Systems and Cybernetics, Nanchang, pp. 274–279 (2012)
Witten, I., Frank, E., Hall, M.: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Boston, Massachusetts (2011)
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Świtoński, A., Piórkowski, B., Josiński, H., Wojciechowski, K., Drabik, A. (2015). Recognition of Human Gestures Represented by Depth Camera Motion Sequences. In: Barbucha, D., Nguyen, N., Batubara, J. (eds) New Trends in Intelligent Information and Database Systems. Studies in Computational Intelligence, vol 598. Springer, Cham. https://doi.org/10.1007/978-3-319-16211-9_4
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DOI: https://doi.org/10.1007/978-3-319-16211-9_4
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16210-2
Online ISBN: 978-3-319-16211-9
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