Abstract
Gesture recognition is an emerging cross-discipline research field, which aims at interpreting human gestures and associating them to a well-defined meaning. It has been used as a mean for supporting human to machine interaction in several applications of robotics, artificial intelligence, and machine learning. In this paper, we propose a system able to recognize human body gestures which implements a constrained training set reduction technique. This allows the system for a real-time execution. The system has been tested on a publicly available dataset of 7,000 gestures, and experimental results have highlighted that at the cost of a little decrease in the maximum achievable recognition accuracy, the required time for recognition can be dramatically reduced.
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
Similar content being viewed by others
Notes
- 1.
A joint is defined as the point of conjunction between two adjacent bones of the human skeleton.
References
Mitra, S., Acharya, T.: Gesture recognition: a survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 37(3), 311–324 (2007)
Starner, T., Auxier, J., Ashbrook, D., Gandy, M.: The gesture pendant: a self-illuminating, wearable, infrared computer vision system for home automation control and medical monitoring. In: The Fourth International Symposium on Wearable Computers (2000)
Davatzikos, C., Ruparel, K., Fan, Y., Shen, D., Acharyya, M., Loughead, J., Gur, R., Langleben, D.D.: Classifying spatial patterns of brain activity with machine learning methods: application to lie detection. Neuroimage 28(3), 663–668 (2005)
Park, S.-B., Yoo, E., Kim, H., Jo, G.-S.: Automatic emotion annotation of movie dialogue using WordNet (2011)
Kang, H., Lee, C.W., Jung, K.: Recognition-based gesture spotting in video games. Pattern Recogn. Lett. 25(15), 1701–1714 (2004)
Picard, R.W., Picard, R.: Affective computing, vol. 252. MIT press, Cambridge (1997)
Gentile, V., Milazzo, F., Sorce, S., Gentile, A., Augello, A., Pilato, G.: Body gestures and spoken sentences: a novel approach for revealing user’s emotions. In: 11th International Conference on Semantic Computing (ICSC 2017) (2017)
De Paola, A., Lo Re, G., Milazzo, F., Ortolani, M.: Adaptable data models for scalable ambient intelligence scenarios. In: International Conference on Information Networking (ICOIN) (2011)
Daidone, E., Milazzo, F.: Short-term sensory data prediction in ambient intelligence scenarios. In: Advances onto the Internet of Things, pp. 89–103. Springer (2014)
Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD workshop, vol. 10, pp. 359–370 (1994)
Malima, A.K., Özgür, E., Çetin, M.: A fast algorithm for vision-based hand gesture recognition for robot control. In: IEEE 14th Signal Processing and Communications Applications, Antalya, Turkey (2006)
Gentile, V., Malizia, A., Sorce, S., Gentile, A.: Designing touchless gestural interactions for public displays in-the-wild. In: Kurosu, M. (ed.) Human-Computer Interaction: Interaction Technologies, pp. 24–34. Springer International Publishing, (2015)
Wu, Y., Huang, T.S.: Vision-based gesture recognition: a review. In: Gesture-Based Communication in Human-Computer Interaction, vol. 1739, pp. 103–115 (2001)
Gentile, V., Sorce, S., Gentile, A.: Continuous hand openness detection using a Kinect-like device. In: Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS), Birmingham, UK (2014)
Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R.: Real-time human pose recognition in parts from single depth images. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition – CVPR 2011 (2011)
Henze, N., Löcken, A., Boll, S., Hesselmann, T., Pielot, M.: Free-hand gestures for music playback: deriving gestures with a user-centred process. In: 9th International Conference on Mobile and Ubiquitous Multimedia (2010)
Sorce, S., Gentile, V., Gentile, A.: Real-time hand pose recognition based on a neural network using microsoft Kinect. In: Eighth International Conference on Broadband and Wireless Computing, Communication and Applications (BWCCA) (2013)
Song, Y., Gu, Y., Wang, P., Liu, Y., Li, A.: A Kinect based gesture recognition algorithm using GMM and HMM. In: 6th International Conference on Biomedical Engineering and Informatics (2013)
Carmona, J.M., Climent, J.: A performance evaluation of HMM and DTW for gesture recognition. In: 17th Iberoamerican Congress (CIARP 2012), Buenos Aires, Argentina (2012)
Shum, H.P., Ho, E.S., Jiang, Y., Takagi, S.: Real-time posture reconstruction for microsoft Kinect. IEEE Trans. Cybern. 43(5), 1357–1369 (2013)
Kasemtaweechok, C., Suwannik, W.: Training set reduction using Geometric Median. In: 15th International Symposium on Communications and Information Technologies (ISCIT) (2015)
Sánchez, J.: High training set size reduction by space partitioning and prototype abstraction. Pattern Recogn. 37(7), 1561–1564 (2004)
Escalera, S., Gonzàlez, J., Barò, X., Reyes, M., Lopes, O., Guyon, I., Athitsos, V., Escalante, H.: Multi-modal gesture recognition challenge 2013: Dataset and results. In: Proceedings of the 15th ACM on International conference on multimodal interaction (2013)
Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Ijcai 14(2), 1137–1145 (1995)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this paper
Cite this paper
Milazzo, F., Gentile, V., Gentile, A., Sorce, S. (2018). Real-Time Body Gestures Recognition Using Training Set Constrained Reduction. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-61566-0_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-61565-3
Online ISBN: 978-3-319-61566-0
eBook Packages: EngineeringEngineering (R0)