A Novel Approach to Key Frame Detection Using Histogram and Dynamic Clustering

Authors

  • Neha Katre Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400057, Maharashtra, India.
  • Meera Narvekar Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400057, Maharashtra, India.
  • Chirag Jagad Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400057, Maharashtra, India.
  • Chirag Jain Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400057, Maharashtra, India.
  • Ishika Chokshi Dwarkadas J. Sanghvi College of Engineering, Mumbai, 400057, Maharashtra, India.

Keywords:

key frame detection, object detection, principal component analysis, YOLO

Abstract

Video analysis is the ability to automatically analyse a video for its temporal and spatial events. Videos consist of frames. Thus, to analyse a video, each and every frame of the video needs to be analysed. High-performance computing is needed for this. Keyframes can be extracted from the videos and used to lessen the computational load. Keyframes are the exemplary frames that contain the significant data needed for analysis. This study suggests a method for identifying the keyframes using the histogram of the frames and dynamic clustering. An average of 97% decrease is obtained in the number of frames required for further analysis.

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Published

2024-05-26

How to Cite

Neha Katre, Meera Narvekar, Chirag Jagad, Chirag Jain, & Ishika Chokshi. (2024). A Novel Approach to Key Frame Detection Using Histogram and Dynamic Clustering. Journal of Computational Analysis and Applications (JoCAAA), 33(06), 1109–1115. Retrieved from https://eudoxuspress.com/index.php/pub/article/view/1049

Issue

Vol. 33 No. 06 (2024): JOCAAA

Section

Articles

License

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.