An Efficient Framework Based on Segmented Block Analysis for Human Activity Recognition

  • Vikas Tripathi
  • Durgaprasad Gangodkar
  • Monika Pandey
  • Vishal Sanserwal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 701)

Abstract

Video surveillance systems core component is human activity recognition. Analysis and identification of human activity emphasize on understanding human behavior in the video. Human activity recognition aims to automatically conjecture the activity being acted by a person. In this paper, we propose a novel feature description algorithm in which a segmented block of logarithm-based motion-generating frames is normalized for analysis of action being performed in the image sequences. The features obtained are classified using random forest classifier. We evaluated the framework on HMDB-51 and ATM datasets and achieved an average accuracy of 58.24 and 93.57%.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Vikas Tripathi
    • 1
  • Durgaprasad Gangodkar
    • 1
  • Monika Pandey
    • 1
  • Vishal Sanserwal
    • 1
  1. 1.Department of Computer Science and EngineeringGraphic Era UniversityDehradunIndia

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