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On Video Based Human Abnormal Activity Detection with Histogram of Oriented Gradients

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Abstract

Video based activity analysis has applications in surveillance and monitoring, and in recent years various automatic analysis techniques were used to obtain efficient detection of activities. As surveillance cameras are becoming ubiquitous, automatic human activity analysis and abnormal activity recognition in videos are required to handle the big streaming data from these sensors. However, automatic video examination remains challenging due to inter-object occlusions in big crowded scenes, unconstrained motions of people’s activities, and also the quality limitation of acquired videos. Further, to attain robust detection of abnormal actions from surveillance videos there needs to be a separate module of computations that can handle vague, infrequent occurring abnormal activities from noise. In this chapter, we propose a method, that can recognize abnormal activities based on the histogram of oriented gradients approach, that can handle the imprecise visual observations, as well as overcome irregularity of other activity recognition approaches. We develop a static camera human detection with local feature extraction and use Support Vector Machine (SVM) for classification of abnormal activity detection from videos. Experimental results indicate the promise of our approach in with good precision with less false positive activity frames.

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Author information

Authors and Affiliations

  1. Department of Computer Science and IT, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Nadeem Iqbal & Malik M. Saad Missen

  2. Department of Mathematics, Khawaja Fareed University of Engineering and Information Technology (KFUIT), Rahim Yar Khan, Pakistan

    Nadeem Salamat

  3. Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA

    V. B. Surya Prasath

  4. Department of Pediatrics, College of Medicine, University of Cincinnati, Cincinnati, OH, USA

    V. B. Surya Prasath

Authors
  1. Nadeem Iqbal
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  2. Malik M. Saad Missen
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  3. Nadeem Salamat
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  4. V. B. Surya Prasath
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Corresponding author

Correspondence to Malik M. Saad Missen .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, NIT Patna, Patna, India

    Amit Kumar Singh

  2. Department of Electronics Engineering, Indian Institute of Technology, BHU, Varanasi, Uttar Pradesh, India

    Anand Mohan

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Iqbal, N., Saad Missen, M.M., Salamat, N., Prasath, V.B.S. (2019). On Video Based Human Abnormal Activity Detection with Histogram of Oriented Gradients. In: Singh, A., Mohan, A. (eds) Handbook of Multimedia Information Security: Techniques and Applications. Springer, Cham. https://doi.org/10.1007/978-3-030-15887-3_21

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  • DOI: https://doi.org/10.1007/978-3-030-15887-3_21

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