Human Activity Recognition from Kinect Captured Data Using Stick Model
In this paper authors have presented a method to recognize basic human activities such as sitting, walking, laying, and standing in real time using simple features to accomplish a bigger goal of developing an elderly people health monitoring system using Kinect. We have used the skeleton joint positions obtained from the software development kit (SDK) of Microsoft as the input for the system. We have evaluated our proposed system against our own data set as well as on a subset of the MSR 3Ddaily activity data set and observed that our proposed method out performs state-of-the-art methods.
KeywordsHuman activity Human action Kinect Skeleton Activity recognition
Unable to display preview. Download preview PDF.
- 3.Aggarwal, J.K., Ryoo, M.S.: Human activity analysis: A review. ACM Computing Surveys (CSUR) 43(3) (April 2011)Google Scholar
- 4.Tapia, E.M.: Activity Recognition in the Home Setting Using Simple and Ubiquitous Sensors., Master degree Thesis, MIT (2003)Google Scholar
- 5.Cheng, H., Liu, Z., Zhao, Y., Ye, G.: Real world activity summary for senior home monitoring. In: IEEE International Conference on Multimedia and Expo (ICME), July 11-15, pp. 1,4 (2011)Google Scholar
- 7.The teardown. Engineering Technology 6(3), 94-95 (April 2011)Google Scholar
- 9.Machida, E., Meifen, C., Murao, T., Hashimoto, H.: Human motion tracking of mobile robot with Kinect 3D sensor. In: Proceedings of SICE Annual Conference (SICE), Akita university, Akita, Japan, August 20-23, pp. 2207–2211 (2012)Google Scholar
- 10.Wang, J., Liu, Z., Wu, Y., Yuan, J.: Mining actionlet ensemble for action recognition with depth cameras. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 16-21, pp. 1290–1297 (2012)Google Scholar
- 11.Le, T., Nguyen, M., Nguyen, T.: Human posture recognition using human skeleton provided by Kinect. In: Proceedings of International Conference on Computing, Management and Telecommunications (ComManTel), January 21-24, pp. 340–345 (2013)Google Scholar
- 12.Yang, X., Tian, Y.: EigenJoints-based Action Recognition Using Naive-Bayes-Nearest-Neighbor. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-21, pp. 14–19 (2012)Google Scholar
- 13.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. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-21, pp. 8–13 (2012)Google Scholar
- 14.Xia, L., Chen, C.-C., Aggarwal, J.K.: View Invariant Human Action Recognition Using Histograms of 3D Joints. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), June 16-21, pp. 20–27 (2012)Google Scholar
- 15.Vapnik, V.N.: Statistical Learning Theory. Wiley, New York (2001)Google Scholar