Abstract
In this paper, we present human pose estimation algorithm that use depth information only. To estimate human poses on a low cost computing platform, we propose a human pose estimation algorithm that mixes a geodesic graph and a support vector machine (SVM). The proposed algorithm can work for any human without calibration and thus anyone can use the system immediately. The SVM-based human pose estimator uses randomly selected human features to reduce computation. The human pose estimation is evaluated through several experiments and the results showed that our approaches perform fairly well.
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References
Correa, P., Marques, F., Marichal, X., Macq, B.: 3d human posture estimation using geodesic distance maps. In: Conference Speech and Computer (SPECOM), pp. 31–34 (2004)
Bellucci, A., Malizia, A., Diaz, P., Aedo, I.: Human-display interaction technology: Emerging remote interfaces for pervasive display environments. IEEE Pervasive Computing 9(2), 72–76 (2010)
Zhang, Z.: Microsoft kinect sensor and its effect. IEEE Multimedia 19(2), 4–10 (2012)
Freedman, B., Shpunt, A., Machline, M., Arieli, Y., et al.: Depth mapping using projected patterns. WO Patent 2,008,120,217 (October 10, 2008)
Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 755–762 (2010)
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. Communications of the ACM Magazine 56(1)
Hernández-Vela, A., Zlateva, N., Marinov, A., Reyes, M., Radeva, P., Dimov, D., Escalera, S.: Graph cuts optimization for multi-limb human segmentation in depth maps. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 726–732 (2012)
Schwarz, L.A., Mkhitaryan, A., Mateus, D., Navab, N.: Human skeleton tracking from depth data using geodesic distances and optical flow. Image and Vision Computing 30(3), 217–226 (2012)
Poppe, R.: Vision-based human motion analysis: An overview. Computer Vision and Image Understanding 108(1), 4–18 (2007)
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M., Moore, R., Kipman, A., Blake, A.: Real-time human pose recognition in parts from single depth images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1–8 (2011)
Cortes, C., Vapnik, V.: Support-vector networks. Machine Learning 20(3), 273–297 (1995)
Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2), 121–167 (1998)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks 13(2), 415–425 (2002)
Aizerman, A., Braverman, E.M., Rozoner, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Amari, S.I., Wu, S.: Improving support vector machine classifiers by modifying kernel functions. Neural Networks 12(6), 783–789 (1999)
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Kim, H., Lee, S., Kim, Y., Lee, D., Ju, J., Myung, H. (2015). Human Pose Estimation Algorithm for Low-Cost Computing Platform Using Depth Information Only. In: Kim, JH., Yang, W., Jo, J., Sincak, P., Myung, H. (eds) Robot Intelligence Technology and Applications 3. Advances in Intelligent Systems and Computing, vol 345. Springer, Cham. https://doi.org/10.1007/978-3-319-16841-8_61
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DOI: https://doi.org/10.1007/978-3-319-16841-8_61
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-16840-1
Online ISBN: 978-3-319-16841-8
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