Robust Feature Based Automated Multi View Human Action Recognition System Using Machine Learning

Authors

  • Paruchuri Yogesh  Department of ECE, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India
  • R. Dillibabu  Department of ECE, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India
  • Palaniappan P  Department of ECE, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India
  • Prof. L. Ashok Kumar  Assistant Professor, Department of ECE, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India
  • Prof. Navarajan  Assistant Professor, Department of ECE, Panimalar Institute of Technology, Poonamallee, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.32628/IJSRSET1961166

Keywords:

MoSIF, BoWs, STIP, EMD, SVM, LST Feature, BI-Linear Interpolation , Classifier, K-Nearest Neighbour, Feature Extraction

Abstract

Automated human action Recognition has the potential to play an important role in Public security. In this project it compares three practical, reliable and generics systems for multiview video based human action recognition namely the nearest classifier, Gaussian mixture model classifier and nearest mean classifier.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2019-04-30

Issue

Volume 6, Issue 2, March-April-2019

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Paruchuri Yogesh, R. Dillibabu, Palaniappan P, Prof. L. Ashok Kumar, Prof. Navarajan "Robust Feature Based Automated Multi View Human Action Recognition System Using Machine Learning" International Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 6, Issue 2, pp.52-58, March-April-2019. Available at doi : https://doi.org/10.32628/IJSRSET1961166