Abstract
Human action recognition is an important research topic that has many potential applications such as video surveillance, human-computer interaction and virtual reality combat training. However, many researches of human action recognition have been performed in single camera system, and has low performance due to vulnerability to partial occlusion. In this paper, we propose a human action recognition system using multiple Kinect sensors to overcome the limitation of conventional single camera based human action recognition system. To test feasibility of the proposed system, we use the snapshot and temporal features which are extracted from three-dimensional (3D) skeleton data sequences, and apply the support vector machine (SVM) for classification of human action. The experiment results demonstrate the feasibility of the proposed system.
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References
Lv, F., Nevatia R.: Single view human action recognition using key pose matching and viterbi path searching. In: Computer Vision and Pattern Recognition, IEEE (2007)
Liu, H., Li, L.: Human action recognition using maximum temporal inter-class dissimilarity. In: The Proceedings of the Second International Conference on Communications, Signal Processing, and Systems, pp. 961–969. Springer International Publishing (2014)
Papadopoulos, G.T., Axenopoulos, A., Daras, P.: Real-time skeleton-tracking-based human action recognition using kinect data. In: Gurrin, C., Hopfgartner, F., Hurst, W., Johansen, H., Lee, H., O’Connor, N. (eds.) MMM 2014, Part I. LNCS, vol. 8325, pp. 473–483. Springer, Heidelberg (2014)
Cheng, Z., Qin, L., Ye, Y., Huang, Q., Tian, Q.: Human daily action analysis with multi-view and color-depth data. In: Fusiello, A., Murino, V., Cucchiara, R. (eds.) ECCV 2012 Ws/Demos, Part II. LNCS, vol. 7584, pp. 52–61. Springer, Heidelberg (2012)
Ni, B., Wang, G., Moulin, P.: RGBD-HuDaAct: a color-depth video database for human daily activity recognition. In: Fossati, A., Gall, J., Grabner, H., Ren, X., Konolige, K. (eds.) Consumer Depth Cameras for Computer Vision, pp. 193–208. Springer, London (2013)
Liu, A.A., Xu, N., Su, Y.T., Lin, H., Hao, T., Yang, Z.X.: Single/multi-view human action recognition via regularized multi-task learning. Neurocomputing 151, 544–553 (2015). Elsevier
Berger, K., Ruhl, K., Schroeder, Y., Bruemmer, C., Scholz, A., Magnor, M.A.: Markerless motion capture using multiple color-depth sensors. In: Vision Modeling, and Visualization, pp. 317–324 (2011)
Haller, E., Scarlat, G., Mocanu, I., Trăscău, M.: Human activity recognition based on multiple kinects. In: Botía, J.A., Álvarez-García, J.A., Fujinami, K., Barsocchi, P., Riedel, T. (eds.) EvAAL 2013. CCIS, vol. 386, pp. 48–59. Springer, Heidelberg (2013)
Junghwan, K., Inwoong, L., Jongyoo, K., Sanghoon, L.: Implementation of an omnidirectional human motion capture system using multiple kinect sensors. In: Computer Science and Engineering Conference, Transactions on Fundamentals of Electronics, Communications and Computer Sciences, IEICE (2015) (submitted)
Zhang, Z.: A flexible new technique for camera calibration. IEEE Trans. Pattern Anal. Mach. Intell. 22(11), 1330–1334 (2000). IEEE
Parisi, G.I., Weber, C., Wermter, S.: Human action recognition with hierarchical growing neural gas learning. In: Wermter, S., Weber, C., Duch, W., Honkela, T., Koprinkova-Hristova, P., Magg, S., Palm, G., Villa, A.E.P. (eds.) ICANN 2014. LNCS, vol. 8681, pp. 89–96. Springer, Heidelberg (2014)
Caillette, F., Howard, T.: Real-time Markerless 3-D Human Body Tracking. University of Manchester (2006)
Castellani, U., Perina, A., Murino, V., Bellani, M., Rambaldelli, G., Tansella, M., Brambilla, P.: Brain morphometry by probabilistic latent semantic analysis. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010, Part II. LNCS, vol. 6362, pp. 177–184. Springer, Heidelberg (2010)
Support Vector Machines - OpenCV 2.4.9.0 documentation. http://docs.opencv.org/2.4.9/modules/ml/doc/support_vector_machines.html
Acknowledgments
This work was supported by the ICT R&D program of MSIP/IITP. [R0101-15-0168, Development of ODM-interactive Software Technology supporting Live-Virtual Soldier Exercises]
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Kwon, B. et al. (2015). Implementation of Human Action Recognition System Using Multiple Kinect Sensors. In: Ho, YS., Sang, J., Ro, Y., Kim, J., Wu, F. (eds) Advances in Multimedia Information Processing -- PCM 2015. PCM 2015. Lecture Notes in Computer Science(), vol 9314. Springer, Cham. https://doi.org/10.1007/978-3-319-24075-6_32
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DOI: https://doi.org/10.1007/978-3-319-24075-6_32
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