Abstract
This paper presents an efficient gesture recognition algorithm based on superpixel distribution and EMD (Earth Mover’s instance) metric with the help of the Kinect depth camera. In the first step we make full use of the depth and skeleton information from Kinect to accurately locate and segment hands from cluttered backgrounds. In the second step we adopt superpixel distribution to describe gestures and the SLIC algorithm works out superpixels. At last EMD metric is applied to measure the distance between two superpixel distributions. And the FC-EMD algorithm is proposed to calculate EMD distance immediately. Experimental results show that the proposed FSP-EMD can speed up and accurately detect hands, extract gesture features and calculate EMD distance. The running time is smaller when compared to the F-EMD and SP-EMD algorithms.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Rayi, Y., et al.: Hand segmentation from depth image using anthropometric approach in natural interface development. Int. J. Sci. Eng. Res (2012)
Zhang, C., Yang, X., Tian, Y.L.: Histogram of 3D facets: a characteristic descriptor for hand gesture recognition. In: 2013 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG), pp. 1–8 (2013)
Mihail, R.P., Jacobs, N., Goldsmith, J.: Real time gesture recognition with 2 kinect sensors. In: International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV) (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. IEEE Conf. Comput. Vis. Pattern Recogn. 2005, 886–893 (2005)
Yao, Y., Fu, Y.: Contour model-based hand-gesture recognition using the kinect sensor. IEEE Trans. Circ. Syst. Video Technol. 24(11), 1935–1944 (2014)
Ren, Z., et al.: Robust part-based hand gesture recognition using kinect sensor. IEEE Trans. Multimed. 15(5), 1110–1120 (2013)
Wang, C., Liu, Z., Chan, S.C.: Superpixel-based hand gesture recognition with kinect depth camera. IEEE Trans. Multimedia 17(1), 29–39 (2015)
Radhakrishna, A., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)
Aiyer, A., et al.: Lloyd clustering of Gauss mixture models for image compression and classification ☆. Signal Process. Image Commun. 20(5), 459–485 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Ming, C., Jianbo, X. (2017). Fast Gesture Recognition Algorithm Based on Superpixel Distribution and EMD Metric. In: Xhafa, F., Patnaik, S., Yu, Z. (eds) Recent Developments in Intelligent Systems and Interactive Applications. IISA 2016. Advances in Intelligent Systems and Computing, vol 541. Springer, Cham. https://doi.org/10.1007/978-3-319-49568-2_38
Download citation
DOI: https://doi.org/10.1007/978-3-319-49568-2_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-49567-5
Online ISBN: 978-3-319-49568-2
eBook Packages: EngineeringEngineering (R0)