Human Activity Recognition Methods 
Dhivya Karunya S1, Krishna Kumar2
1Dhivya Karunya S*, ECE, S.E.A College of Engineering and Technology, Visvesvaraya Technological University, Bangalore, Karnataka, India.
2Krishna Kumar, ECE, Gopalan College of Engineering and Management, Visvesvaraya Technological University, Bangalore, Karnataka, India.
Manuscript received on June 01, 2020. | Revised Manuscript received on June 08, 2020. | Manuscript published on June 30, 2020. | PP: 1024-1028 | Volume-9 Issue-5, June 2020. | Retrieval Number: E9771069520/2020©BEIESP | DOI: 10.35940/ijeat.E9771.069520
Open Access | Ethics and Policies | Cite | Mendeley
© 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 in a video based application plays a significant role that alerts the researchers towards recognizing the motion of human. Other video applications also have video content extraction, summarization, and human computer interactions. The existing methods needs manual footnote of pertinent portion of actions of our interest. Recognition of human action can be done authentic without physical commentary of applicable parts of action of any one’s interest. In this paper we try to update the previous reviews on many ways of recognizing Human activities in videos that had different techniques like Hidden Markov model, feature extraction, segmentation etc. the recognition of human activity in applications like visual observation in mobile, human fall detection, video conference, robotics.
Keywords: Human action Recognition, Spatial and Temporal extent, Dense Trajectory, Support Vector Machines, Spatio-Temporal Interest Points, Hidden Markov models.