Survey On Human Motion Recognition

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2019 by IJETT Journal
Volume-67 Issue-10
Year of Publication : 2019
Authors : Bhavana R Maale, Roopa Guttedar
DOI :  10.14445/22315381/IJETT-V67I10P204


MLA Style: Bhavana R Maale, Roopa Guttedar  "Survey On Human Motion Recognition" International Journal of Engineering Trends and Technology 67.10 (2019):17-19

APA Style:Bhavana R Maale, Roopa Guttedar. Survey On Human Motion Recognition International Journal of Engineering Trends and Technology, 67(10),17-19.

The human pose estimation can be improved over images based on estimation methods. It presents a method to estimate a sequence of human poses in unconstrained videos. The aims to do demonstrate by using temporal information. It is based on two main ideas: ’Abstraction’ and ‘Association’ to impose the intra-and inter-frame body part constraints. The concept of abstraction body part is introduced to metaphysical combine the symmetric body parts and model them in tree based body part structure. the second method ‘Association’ the optimal tracklets are generated for each abstract body part ,in order to enforce the spatiotemporal constraints between body parts in adjacent frames.


[1] Bobick, A. F., & Davis, J. W. (2001). The recognition of human movement using temporal templates. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23(3), 257–267. >
[2] Chaaraoui, A., Padilla-Lopez, J., & Flórez-Revuelta, F. (2013). Fusion of skeletal and silhouette-based features for human action recognition with rgb-d devices. >
[3] Johansson, G. (1973). Visual perception of biological motion and a model for its analysis. Perception & Psychophysics, 14(2), 201–211. >
[4] Laptev, I., & Lindeberg, T. (2004, August). Velocity adaptation of space-time interest points. Proceedings of the 17th International Conference onPattern Recognition ICPR ‘04 (Vol. 1, pp. 52-56). >
[5] Ryoo, M. S., & Aggarwal, J. K. (2009). Semantic representation and recognition of continued and recursive human activities. International Journal of Computer Vision, 82(1), 1–24. doi:10.1007/s11263-008-0181-1. >
[6] Shechtman, E., & Irani, M. (2005, June). Space-time behavior based correlation. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition CVPR ‘05 (Vol. 1, pp. 405-412). >
[7] Pham, C. H., Le, Q. K., & Le, T. H. (2014). Human action recognition using dynamic time warping and voting algorithm. VNU Journal of Science: Computer Science and Communication Engineering.

Human pose estimation ,motion detection, object detection