Abstract
Every form of human gesture has been recognized in the literature as a means of providing natural and intuitive ways to interact with computers across many computer application domains. In this paper we propose a real time gesture recognition approach which uses a depth sensor to extract the initial human skeleton. Then, robust and significant features have been compared and the most unrelated and representative features have been selected and fed to a set of supervised classifiers trained to recognize different gestures. Different problems concerning the gesture initialization, segmentation, and normalization have been considered. Several experiments have demonstrated that the proposed approach works effectively in real time applications.
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
Almetwally, I., Mallem, M.: Real-time tele-operation and tele-walking of humanoid robot Nao using Kinect depth camera. In: Proceedings of 10th IEEE International Conference on Networking, Sensing and Control (ICNSC), pp. 463–466 (2013)
Army: Visual signals: Arm-and-hand signals for ground forces. Field Manual 21–60, Washington, DC (September 1987), Headquarter Department of the Army
den Bergh, M.V., Carton, D., de Nijs, R., Mitsou, N., Landsiedel, C., Kuehnlenz, K., Wollherr, D., Gool, L.V., Buss, M.: Real-time 3d hand gesture interaction with a robot for understanding directions from humans. In: 20th IEEE International Symposium on Robot and Human Interactive Communication, pp. 357–362 (2011)
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 (EAIT), pp. 348–351 (2012)
Biswas, K., Basu, S.: Gesture recognition using microsoft kinect. In: 5th International Conference on Automation, Robotics and Applications (ICARA), pp. 100–103 (2011)
Bodiroža, S., Doisy, G., Hafner, V.: Position-invariant, real-time gesture recognition based on dynamic time warping. In: Proceedings of the 8th ACM/IEEE International Conference on Human-Robot Interaction, pp. 87–88. IEEE Press (2013)
Castiello, C., D’Orazio, T., Fanelli, A., Spagnolo, P., Torsello, M.: A model free approach for posture classificatin. In: IEEE Conference on Advances Video and Signal Based Surveillance, AVSS (2005)
Cruz, L., Lucio, F., Velho, L.: Kinect and RGBD images: Challenges and applications. In: XXV SIBGRAPI IEEE Confernce and Graphics, Patterns and Image Tutorials, pp. 36–49 (2012)
Gu, Y., Do, H., Ou, Y., Sheng, W.: Human gesture recognition through a Kinect sensor. In: IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1379–1384. IEEE (2012)
Hachaj, T., Ogiela, M.: Rule-based approach to recognizing human body poses and gestures in real time. Multimedia Syst. 20, 81–99 (2014)
Iosifidis, A., Tefas, A., Pitas, I.: View invariant action recognition based on artificial neural networks. IEEE Trans. Neural Netw. Learn. Syst. 23(3), 412–424 (2012)
Iosifidis, A., Tefas, A., Pitas, I.: Multi view action recognition based on action volumes fuzzy distances and cluster discriminant analysis. Sig. Process. 93, 1445–1457 (2013)
Oh, J., Kim, T., Hong, H.: Using binary decision tree and multiclass svm for human gesture recognition. In: International Conference on Information Science and Applications (ICISA), pp. 1–4 (2013)
Lai, K., Konrad, J., Ishwar, P.: A gesture-driven computer interface using kinect. In: IEEE Southwest Symposium on Image Analysis and Interpretation (SSIAI), pp. 185–188 (2012)
Leo, M., Spagnolo, P., D’Orazio, T., Distante, A.: Human activity recognition in archaeological sites by hidden markov models. In: Aizawa, K., Nakamura, Y., Satoh, S. (eds.) PCM 2004. LNCS, vol. 3332, pp. 1019–1026. Springer, Heidelberg (2004)
Miranda, L., Vieira, T., Martinez, D., Lewiner, T., Vieira, A., Campos, M.: Real-time gesture recognition from depth data through key poses learning and decision forests. In: 25th SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 268–275 (2012)
Saponaro, G., Salvi, G., Bernardino, A.: Robot anticipation of human intentions through continuous gesture recognition. In: International Conference on Collaboration Technologies and Systems (CTS), pp. 218–225. IEEE (2013)
Shoemake, K.: Animating rotation with quaternion curves. In: SIGGRAPH’85 Proceedings of the 12th Annual Conference on Computer Graphics and Interactive Techniques, vol. 19(3), pp. 245–254 (1985)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Attolico, C., Cicirelli, G., Guaragnella, C., D’Orazio, T. (2015). A Real Time Gesture Recognition System for Human Computer Interaction . In: Schwenker, F., Scherer, S., Morency, LP. (eds) Multimodal Pattern Recognition of Social Signals in Human-Computer-Interaction. MPRSS 2014. Lecture Notes in Computer Science(), vol 8869. Springer, Cham. https://doi.org/10.1007/978-3-319-14899-1_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-14899-1_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-14898-4
Online ISBN: 978-3-319-14899-1
eBook Packages: Computer ScienceComputer Science (R0)