Abstract
Most existing gesture recognition algorithms use fixed postures in simple environments. In natural calling, the user may perform gestures in various positions and the environment may be occupied by many people with many hand motions. This paper presents an algorithm for natural calling gesture recognition by detecting gaze, the position of the hand-wrist, and fingertips. A challenge to solve is how to make the natural calling gesture recognition work in crowded environments with randomly moving objects. The approach taken here is to get the key-points of individual people using a real-time detector by using a camera and detect gaze and hand-wrist positions. Then, zooming into the hand-wrist part and getting the key-points of fingertips, we calculate the positions of the fingertips to recognize calling gestures. We tested the proposed approach in video under different conditions: from one person to over four people that sit and walk around. We obtained an average recognition accuracy of 87.12%, thus showing the effectiveness of our approach.
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References
Lemaignan, S., Warnier, M., Sisbot, E.A., Clodic, A., Alami, R.: Artificial cognition for social human–robot interaction: an implementation. Artif. Intell. 247, 45–69 (2017)
Escalera, S., Athitsos, V., Guyon, I.: Challenges in multimodal gesture recognition. J. Mach. Learn. Res. 17(1), 2549–2602 (2016)
Zhao, X., Naguib, A.M., Lee, S.: Kinect based calling gesture recognition for taking order service of elderly care robot. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication 2014 RO-MAN, pp. 525–530, August 2014
Canal, G., Angulo, C., Escalera, S.: Gesture based human multirobot interaction. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8, July 2015
Cicirelli, G., Attolico, C., Guaragnella, C., D’Orazio, T.: A kinect-based gesture recognition approach for a natural human robot interface. Int. J. Adv. Rob. Syst. 12 (2015)
Gu, Y., Do, H., Ou, Y., Sheng, W.: Human gesture recognition through a kinect sensor. In: 2012 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1379–1384, December 2012
Zhou, Y., Jiang, G., Lin, Y.: A novel finger and hand pose estimation technique for real-time hand gesture recognition. Pattern Recogn. 49, 102–114 (2016)
Hidalgo, G., Cao, Z., Simon, T., Wei, S.-E., Joo, H., Sheik, Y.: Openpose. https://github.com/CMU-Perceptual-Computing-Lab/openpose
Acknowledgement
This work was supported by JSPS KAKENHI Grant Number JP26240038.
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Phyo, A.S., Fukuda, H., Lam, A., Kobayashi, Y., Kuno, Y. (2018). Natural Calling Gesture Recognition in Crowded Environments. In: Huang, DS., Bevilacqua, V., Premaratne, P., Gupta, P. (eds) Intelligent Computing Theories and Application. ICIC 2018. Lecture Notes in Computer Science(), vol 10954. Springer, Cham. https://doi.org/10.1007/978-3-319-95930-6_2
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DOI: https://doi.org/10.1007/978-3-319-95930-6_2
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