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casa 24(1):

Editorial

Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques

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  • @ARTICLE{10.4108/eetcasa.5131,
        author={Vijayalaxmi N Rathod and Anjali H Kugate and Bhimambika  Y Balannanavar and R.H Goudar and Dhananjaya G M and Anjanabhargavi Kulkarni and Geeta Hukkeri},
        title={Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques},
        journal={EAI Endorsed Transactions on Contex-aware Systems and Applications},
        volume={10},
        number={1},
        publisher={EAI},
        journal_a={CASA},
        year={2024},
        month={7},
        keywords={Video summarization, Key Extraction, Edge Detection, Motion Analysis, Key frames},
        doi={10.4108/eetcasa.5131}
    }
    
  • Vijayalaxmi N Rathod
    Anjali H Kugate
    Bhimambika Y Balannanavar
    R.H Goudar
    Dhananjaya G M
    Anjanabhargavi Kulkarni
    Geeta Hukkeri
    Year: 2024
    Efficient Key Frame Extraction from Videos Using Convolutional Neural Networks and Clustering Techniques
    CASA
    EAI
    DOI: 10.4108/eetcasa.5131
Vijayalaxmi N Rathod1,*, Anjali H Kugate2, Bhimambika Y Balannanavar2, R.H Goudar2, Dhananjaya G M2, Anjanabhargavi Kulkarni2, Geeta Hukkeri2
  • 1: Visvesveraya Technological University
  • 2: Visvesvaraya Technological University Belagavi
*Contact email: vijaylaxmirathod@gmail.com

Abstract

One of the most reliable information sources is video, and in recent years, online and offline video consumption has increased to an unprecedented degree. One of the main difficulties in extracting information from videos is that unlike images, where information can be gleaned from a single frame, a viewer must watch the entire video in order to comprehend the context. In this work, we try to use various algorithmic techniques, such as deep neural networks and local features, in conjunction with a variety of clustering techniques, to find an efficient method of extracting interesting key frames from videos to summarize them. Video summarization plays a major role in video indexing, browsing, compression, analysis, and many other domains. One of the fundamental elements of video structure analysis is key frame extraction, which pulls significant frames out of the movie. An important frame from a video that may be used to summarize videos is called a key frame. We provide a technique that leverages convolutional neural networks in our suggested model, static video summarization, and key frame extraction from movies.

Keywords
Video summarization, Key Extraction, Edge Detection, Motion Analysis, Key frames
Received
2024-07-18
Accepted
2024-07-18
Published
2024-07-18
Publisher
EAI
http://dx.doi.org/10.4108/eetcasa.5131

Copyright © 2024 Kugate et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NC-SA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.

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