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Shot Boundary Detection from Lecture Video Sequences Using Histogram of Oriented Gradients and Radiometric Correlation

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Smart Computer Vision

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

Abstract

In this chapter, we put forward a new technique for lecture video segmentation and key frame extraction. In this chapter, the advantages of Histogram of Oriented Gradients (HOG) features and radiometric correlation with entropic measures are explored to detect the shot boundaries and the key frames of the lecture video sequences. In the initial stage of the algorithm, HOG feature is used to project all frames into an n-dimensional feature space. The similarities between the n-dimensional extracted HOG features for two consecutive frames are obtained using radiometric correlation measure. The radiometric correlation between the successive frames of the video is found to have a significant amount of uncertainty, due to variation in color, illumination, or object motion. We have used entropic measure to find the shot boundaries. The key frames are obtained after detection of the shot boundaries by analyzing the peaks and valleys of the radiometric correlation measures. The proposed scheme is tested on several lecture video sequences and compared against six existing state-of-the-art techniques by considering two evaluation measures: computational time and shot transitions.

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Author information

Authors and Affiliations

  1. Department of Electronics and Communication Engineering, National Institute of Technology Goa, Farmagudi, Ponda, Goa, India

    T. Veerakumar, K. Sandeep Kumar & Nikhil O. F. Da Rocha

  2. Department of Electrical Engineering, Indian Institute of Technology Jammu, Nagrota, Jammu, India

    Badri Narayan Subudhi

  3. Department of Instrumentation and Control Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India

    S. Esakkirajan

Authors
  1. T. Veerakumar
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  2. Badri Narayan Subudhi
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  3. K. Sandeep Kumar
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  4. Nikhil O. F. Da Rocha
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  5. S. Esakkirajan
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Corresponding author

Correspondence to T. Veerakumar .

Editor information

Editors and Affiliations

  1. PSG College of Technology, Coimbatore, Tamil Nadu, India

    B. Vinoth Kumar

  2. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    P. Sivakumar

  3. Thiruvettakudy, National Institute of Technology, Puducherry, Karaikal, India

    B. Surendiran

  4. University of North Texas, Denton, TX, USA

    Junhua Ding

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Veerakumar, T., Subudhi, B.N., Kumar, K.S., Da Rocha, N.O.F., Esakkirajan, S. (2023). Shot Boundary Detection from Lecture Video Sequences Using Histogram of Oriented Gradients and Radiometric Correlation. In: Kumar, B.V., Sivakumar, P., Surendiran, B., Ding, J. (eds) Smart Computer Vision. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-031-20541-5_2

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  • DOI: https://doi.org/10.1007/978-3-031-20541-5_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20540-8

  • Online ISBN: 978-3-031-20541-5

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