Abstract
Human action recognition is an important yet challenging task. With the introduction of RGB-D sensors, human body joints can be extracted with high accuracy, and skeleton-based action recognition has been investigated and gained some success. In this paper, we split an entire action trajectory into several segments and represent each segment using covariance descriptor of joints’ coordinates. We further employ the projective dictionary pair learning (PDPL) and majority-voting for multi-class action classification. Experimental results on two benchmark datasets demonstrate the effectiveness of our approach.
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References
Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: a review. ACM Comput. Surv. 43(3), 16 (2011)
Bashir, F., Khokhar, A., Schonfeld, D.: Automatic object trajectory-based motion recognition using gaussian mixture models. In: 2012 IEEE International Conference on Multimedia and Expo, pp. 1532–1535 (2005)
Chen, L., Wei, H., Ferryman, J.M.: A survey of human motion analysis using depth imagery. Pattern Recogn. Lett. 34, 1995–2006 (2013)
Faria, D.R., Premebida, C., Nunes, U.: A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In: IEEE International Symposium on Robot and Human Interactive Communication, pp. 732–737. IEEE (2014)
Gaglio, S., Re, G.L., Morana, M.: Human activity recognition process using 3-D posture data. IEEE Trans. Hum.-Mach. Syst. 45(5), 586–597 (2014)
Gu, S., Zhang, L., Zuo, W., Feng, X.: Projective dictionary pair learning for pattern classification. In: Advances in Neural Information Processing Systems, pp. 793–801 (2014)
Han, J.: Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans. Cybern. 43(5), 1318–1334 (2013)
Hussein, M.E., Torki, M., Gowayyed, M.A., El-Saban, M.: Human action recognition using a temporal hierarchy of covariance descriptors on 3D joint locations. In: The 23rd International Joint Conference on Artificial Intelligence (2013)
Johansson, G.: Visual motion perception. Sci. Am. 232(6), 76–88 (1975)
Koppula, H.S., Gupta, R., Saxena, A.: Learning human activities and object affordances from RGB-D videos. Int. J. Robot. Res. 32(8), 951–970 (2013)
Li, W., Zhang, Z., Liu, Z.: Action recognition based on a bag of 3D points. Workshop on Human Activity Understanding from 3D Data, pp. 9–14 (2010)
Lv, F., Nevatia, R.: Recognition and segmentation of 3-D human action using HMM and multi-class AdaBoost. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006. LNCS, vol. 3954, pp. 359–372. Springer, Heidelberg (2006)
Mairal, J., Bach, F., Ponce, J., Sapiro, G., Zisserman, A.: Supervised dictionary learning. In: arXiv preprint arXiv:0809.3083 (2008)
Muller, M., Roder, T.: Motion templates for automatic classification and retrieval of motion capture data. In: Proceedings of the ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 137–146. Eurographics Association Aire-la-Ville, Switzerland (2006)
Ni, B., Pei, Y., Moulin, P., Yan, S.: Multilevel depth and image fusion for human activity detection. IEEE Trans. Cybern. 43(5), 1383–1394 (2013)
Shan, J., Akella, S.: 3D human action segmentation and recognition using pose kinetic energy. In: IEEE Workshop on Advanced Robotics and Its Social Impacts, pp. 69–75 (2014)
Sivalingam, R., Somasundaram, G., Bhatawadekar, V., Morellas, V., Papanikolopoulos, N.: Sparse representation of point trajectories for action classification. In: IEEE International Conference on Robotics and Automation, pp. 3601–3606 (2012)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Unstructured human activity detection from RGBD images. In: IEEE International Conference on Robotics and Automation, pp. 842–849 (2012)
Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.M.: STOP: space-time occupancy patterns for 3D action recognition from depth map sequences. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.) CIARP 2012. LNCS, vol. 7441, pp. 252–259. Springer, Heidelberg (2012)
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Learning actionlet ensemble for 3D human action recognition. IEEE Trans. Pattern Anal. Mach. Intell. 36(5), 914–927 (2014)
Wu, O., Li, Y.F., Zhang, J.: A hierarchical motion trajectory signature descriptor. In: IEEE International Conference on Robotics and Automation, pp. 3070–3075 (2008)
Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3D joints. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 20–27 (2012)
Yang, X., Tian, Y.L.: Eigenjoints-based action recognition using Naive-Bayes-nearest-neighbor. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 14–19 (2012)
Yang, X., Tian, Y.L.: Effective 3D action recognition using eigenjoints. J. Visual Commun. Image Represent. 25(1), 2–11 (2014)
Ye, M., Zhang, Q., Wang, L., Zhu, J., Yang, R., Gall, J.: A survey on human motion analysis from depth data. In: Grzegorzek, M., Theobalt, C., Koch, R., Kolb, A. (eds.) Time-of-Flight and Depth Imaging. LNCS, vol. 8200, pp. 149–187. Springer, Heidelberg (2013)
Yuan, J., Wu, Y., Liu, Z., Wang, J.: Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1290–1297 (2012)
Zanfir, M., Leordeanu, M., Sminchisescu, C.: The moving pose: an efficient 3D kinematics descriptor for low-latency action recognition and detection. In: IEEE International Conference on Computer Vision, pp. 2752–2759 (2013)
Zhang, C., Tian, Y.: RGB-D camera-based daily living activity recognition. J. Comput. Vis. Image Process. 2(4), 12 (2012)
Zhu, Y., Chen, W., Guo, G.: Evaluating spatiotemporal interest point features for depth-based action recognition. Image Vis. Comput. 32(8), 453–464 (2014)
Acknowledgments
This work is supported by the National Natural Science Foundation of China under Project 61175116, and Shanghai Knowledge Service Platform for Trustworthy Internet of Things (No. ZF1213).
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Xiang, Y., Xu, J. (2015). Discriminative Dictionary Learning for Skeletal Action Recognition. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_58
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