Abstract
The paper presents a method to automatically separate consecutive human actions into subsegments and recognize them. The 3D positions of the joints tracked by depth camera like Kinect sensors and the depth motion maps (DMMs) are used in the method. Both of the two types of data contain useful information to help us extract features for each action video. However, they are also full of noise. So we combine the pairwise relative positions of the 3D joints (Skeleton Joints) and Histograms of Oriented Gradients (HOG) calculated from the DMM together to improve the feature representation. A SVM-based classification ensemble is built to achieve the recognition result. We also build a Probability-Distribution-Difference (PDD) based dynamic boundary detection framework to segment consecutive actions before applying recognition. The segmentation framework is online and reliable. The experimental results applied to the Microsoft Research Action3D dataset outperform the state of the art methods.
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Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3d points. In: 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 9–14. IEEE (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 56, 116–124 (2013)
Li, W., Zhang, Z., Liu, Z.: Expandable data-driven graphical modeling of human actions based on salient postures. IEEE Transactions on Circuits and Systems for Video Technology 18, 1499–1510 (2008)
Zhang, S.: Recent progresses on real-time 3d shape measurement using digital fringe projection techniques. Optics and Lasers in Engineering 48, 149–158 (2010)
Weinland, D., Ronfard, R., Boyer, E.: A survey of vision-based methods for action representation, segmentation and recognition. Computer Vision and Image Understanding 115, 224–241 (2011)
Yang, X., Tian, Y.: Eigenjoints-based action recognition using naive-bayesnearest-neighbor. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 14–19. IEEE (2012)
Yang, X., Zhang, C., Tian, Y.: Recognizing actions using depth motion maps-based histograms of oriented gradients. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1057–1060. ACM (2012)
Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y.: Robust 3D action recognition with random occupancy patterns. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012, Part II. LNCS, vol. 7573, pp. 872–885. Springer, Heidelberg (2012)
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1290–1297. IEEE (2012)
Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005, vol. 1, pp. 886–893. IEEE (2005)
Wu, D., Zhu, F., Shao, L.: One shot learning gesture recognition from rgbd images. In: 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 7–12. IEEE (2012)
Rubin, J.M., Richards, W.A.: Boundaries of visual motion (1985)
Bradski, G., Kaehler, A.: Learning OpenCV: Computer vision with the OpenCV library. O’Reilly (2008)
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning 36, 105–139 (1999)
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Yang, R., Yang, R. (2014). Action Segmentation and Recognition Based on Depth HOG and Probability Distribution Difference. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theory. ICIC 2014. Lecture Notes in Computer Science, vol 8588. Springer, Cham. https://doi.org/10.1007/978-3-319-09333-8_82
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DOI: https://doi.org/10.1007/978-3-319-09333-8_82
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