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Computational Intelligence in Data Mining - Volume 2 pp 111–122Cite as

An Efficient Multi-view Based Activity Recognition System for Video Surveillance Using Random Forest

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Part of the book series: Smart Innovation, Systems and Technologies ((SIST,volume 32))

Abstract

Vision-based human activity recognition is an emerging field and have been actively carried out in computer vision and artificial intelligence area. However, human activity recognition in a multi-view environment is a challenging problem to solve, the appearance of a human activity varies dynamically, depending on camera viewpoints. This paper presents a novel and proficient framework for multi-view activity recognition approach based on Maximum Intensity Block Code (MIBC) of successive frame difference. The experiments are carried out using West Virginia University (WVU) multi-view activity dataset and the extracted MIBC features are used to train Random Forest for classification. The experimental results exhibit the accuracies and effectiveness of the proposed method for multi-view human activity recognition in order to conquer the viewpoint dependency. The main contribution of this paper is the application of Random Forests classifier to the problem of multi-view activity recognition in surveillance videos, based only on human motion.

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Authors and Affiliations

  1. Speech and Vision Lab, Department of Computer Science and Engineering, Faculty of Engineering and Technology, Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu, India

    J. Arunnehru & M. K. Geetha

Authors
  1. J. Arunnehru
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  2. M. K. Geetha
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Corresponding author

Correspondence to J. Arunnehru .

Editor information

Editors and Affiliations

  1. University of Canberra, Canberra, Australia and University of South Australia, Adelaide, South Australia, Australia

    Lakhmi C. Jain

  2. Department of Computer Science and Engineering, Veer Surendra Sai University of Technology, Sambalpur, Odisha, India

    Himansu Sekhar Behera

  3. Computer Science & Engineering, Kalyani University, Nadia, West Bengal, India

    Jyotsna Kumar Mandal

  4. Dept. of Computer Science and Engineering, National Institute of Technology Rourkela, Rourkela, India

    Durga Prasad Mohapatra

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Arunnehru, J., Geetha, M.K. (2015). An Efficient Multi-view Based Activity Recognition System for Video Surveillance Using Random Forest. In: Jain, L., Behera, H., Mandal, J., Mohapatra, D. (eds) Computational Intelligence in Data Mining - Volume 2. Smart Innovation, Systems and Technologies, vol 32. Springer, New Delhi. https://doi.org/10.1007/978-81-322-2208-8_12

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  • DOI: https://doi.org/10.1007/978-81-322-2208-8_12

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-2207-1

  • Online ISBN: 978-81-322-2208-8

  • eBook Packages: EngineeringEngineering (R0)

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