A Fine Grainedresearch Over Human Action Recognition
S. Sandhya Rani1, G. Appa Rao Naidu2, V. Usha Shree3
1S. Sandhya Rani, Assoc.Prof at MREC(A), Research Scholer JNTUH, Department of CSE.
2Dr. G. Appa Rao Naidu, Professor at JBIET, Department of CSE.
3Dr.V. Usha Shree, Principal at JBREC, Department of ECE.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 5376-5384 | Volume-9 Issue-1, November 2019. | Retrieval Number: A4677119119/2019©BEIESP | DOI: 10.35940/ijitee.A4677.119119
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Human Action Recognition from videos has been an active research is in the computer vision due to its significant applicability in various real-time applications like video retrieval, human-robot interactions, and visual surveillance, etc. Though there are so many surveys over Human action Recognition, they are limited to various constraints like only focusing on the methods in few orientations only. Unlike the earlier ones, this paper provides a detailed survey according to the basic working methodology of Human action recognition system. Initially, a detailed illustration is given about various standard benchmark datasets. Further, following the methodology, the survey is accomplished in two phases, i.e., the survey over feature extraction approaches and the survey over action classification approaches. Further, a fine-grained survey is also accomplished under every phase based on the individual strategies
Keywords: Human Action Recognition, Feature Extraction, Classification, Spatio-temporal Interest Points, Trajectories, Support Vector Machine, Deep Learning, Action Datasets.
Scope of the Article: Classification