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Using Attractive–Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1393))

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Abstract

Background subtraction being one of the most crucial steps in numerous real-world video-based applications has been studied extensively to date. Several researchers have provided solutions to this problem by exploiting the patterns and information hidden in a set of initial raw videoframes with the help of sophisticated machine learning techniques. While others have proposed to uncover the hidden patterns in raw data by employing a variety of texture patterns, which now have become an inseparable part of the background subtraction problem. Hence, the paper proposes to employ a novel feature combination of Attractive–repulsive local binary gradient contours with RGB channels and luminance information into a consensus-based modeling technique. The results obtained on sample videos from the standard Change Detection dataset [1] support for the superiority of the proposed methodology.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, India

    Rimjhim Padam Singh

  2. Department of Computer Science and Engineering, Visvesvaraya National Institue of Technology, Nagpur, India

    Poonam Sharma

Authors
  1. Rimjhim Padam Singh
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  2. Poonam Sharma
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Corresponding author

Correspondence to Rimjhim Padam Singh .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Aruna Tiwari

  2. Computer Science and Engineering, Indian Institute of Technology Indore, Indore, Madhya Pradesh, India

    Kapil Ahuja

  3. Dr. B. R. Ambedkar National Institute of Technology, Jalandhar, India

    Anupam Yadav

  4. Department of Mathematics, South Asian University, New Delhi, India

    Jagdish Chand Bansal

  5. Department of Mathematics, Indian Institute of Technology Roorkee, Roorkee, India

    Kusum Deep

  6. School of Mathematics, Computer Science and Engineering, Liverpool Hope University, Liverpool, UK

    Atulya K. Nagar

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Singh, R.P., Sharma, P. (2021). Using Attractive–Repulsive Binary Local Gradient Contours for Sample-Consensus Background Modeling. In: Tiwari, A., Ahuja, K., Yadav, A., Bansal, J.C., Deep, K., Nagar, A.K. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 1393. Springer, Singapore. https://doi.org/10.1007/978-981-16-2712-5_54

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