Abstract
Human pose estimation is mainly used for the purpose of training the robots to incorporate in a way which the actions are performed in reality. The human pose estimation is the highly exploring topic in the field of computer vision research. The objective of the dynamic pose estimation is to estimate the human pose in all the available datasets. It begins with mapping the skeletal coordinates and also visualizing the coordinates. The features within the coordinates are being obtained with the help of proportionate distance features and the divergence between the coordinates. The feature extraction has taken place separately for RGB and depth dataset. The computed data is being present in the same format as that of the vector space that is being allocated for the feature extractor. The classifiers such as support vector machines (SVMs), k-nearest neighbor (KNN), and the decision tree are being used. The major use of this learning process is to help the robot to train it similar to that of the human functionalities. This functionality can be used in any learning and training procedures to build the human-robotic system. The proper understanding of how every action takes place is learned in this process and thereby training any system with similar measures is simplified with this procedure.
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References
Lin, Y.C., Hu, M.C., Cheng, W.H., Hsieh, Y.H., Chen, H.M.: Human action recognition and retrieval using sole depth information. In: Proceedings of the 20th ACM International Conference on Multimedia, pp. 1053–1056
Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1290–1297 (2012)
Arunnehru, J., Geetha, M.K.: Internet of things based intelligent elderly care system. Internet Things: Nov. Adv. Envisioned Appli. 207–229 (2017)
Ni, B., Wang, G., Moulin, P.: A color-depth video database for human daily activity recognition. IEEE international conference on computer vision workshops (ICCV workshops), vol. 2011, pp. 1147–1153 (2017)
Megavannan, V., Agarwal, B., Babu, R.V.: Human action recognition using depth maps. In: International Conference on Signal Processing and Communications (SPCOM), vol. 2012, pp. 1–5 (2012)
Uddin, M.Z., Thang, N.D., Kim, J.T., Kim, T.S.: Human activity recognition using body joint angle features and hidden markov model. Etri J. 33(4), 569–579 (2011)
Wang, J.J.Y., Bensmail, H., Gao, X.: Joint learning and weighting of visual vocabulary for bag-of-feature based tissue classification. Pattern Recognit. 46(12), 3249–3255 (2013)
Jalal, A., Kim, J.T., Kim, T.S.: Human activity recognition using the labeled depth body parts information of depth silhouettes. In: Proceedings of the 6th International Symposium on Sustainable Healthy Buildings, p. 27. Seoul, Korea (2012)
Ofli, F., Chaudhry, R., Kurillo, G., Vidal, R., Bajcsy, R.: Sequence of the most informative joints (smij): a new representation for human skeletal action recognition. J. Vis. Commun. Image Represent. 25(1), 24–38 (2014)
Xia, L., Chen, C.C., Aggarwal, J.K.: View invariant human action recognition using histograms of 3d joints. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, pp. 20–27 (2014)
Gao, Z., Zhang, H., Liu, A.A., Xue, Y.B., Xu, G.P.: Human action recognition using pyramid histograms of oriented gradients and collaborative multi-task learning. KSII Trans. Internet Inf. Syst. 8(2) (2014)
Gao, Z., Song, J.M., Zhang, H., Liu, A.A., Xue, Y.B., Xu, G.P.: Human action recognition via multi-modality information. J. Electr. Eng. Technol. 9(2), 739–748 (2014)
Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.: Stop: Space-time occupancy patterns for 3d action recognition from depth map sequences. Iberoamerican Congress on Pattrn Recognition, pp. 252–259 (2012)
Sung, J., Ponce, C., Selman, B., Saxena, A.: Human activity detection from RGBD images. In: Workshops at the Twenty-Fifth AAAI Conference on Artificial (2011). Intelligence
Samanthula, B.K., Elmehdwi, Y., Jiang, W.: K-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)
Navada, A., Ansari, A.N., Patil, S. and Sonkamble, B.A.: Overview of use of decision tree algorithms in machine learning. IEEE Control. Syst. Grad. Res. Colloq. 37–42 (2011)
Zhu, Y., Chen, W., Guo, G.: Evaluating spatiotemporal interest point features for depth-based action recognition. Image Vis. Comput. 32(8), 453–464 (2014)
Faria, D.R., Premebida, C., Nunes, U.: A probabilistic approach for human everyday activities recognition using body motion from RGB-D images. In: The 23rd IEEE International Symposium on Robot and Human Interactive Communication, pp. 732–737 (2014)
Shan, J., Akella, S.: 3D human action segmentation and recognition using pose kinetic energy. IEEE International Workshop on Advanced Robotics and Its Social Impacts, pp. 69–75 (2014)
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Arunnehru, J., Nandhana Davi, A.K., Sharan, R.R., Nambiar, P.G. (2020). Human Pose Estimation and Activity Classification Using Machine Learning Approach. In: Reddy, V., Prasad, V., Wang, J., Reddy, K. (eds) Soft Computing and Signal Processing. ICSCSP 2019. Advances in Intelligent Systems and Computing, vol 1118. Springer, Singapore. https://doi.org/10.1007/978-981-15-2475-2_11
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