An effective algorithm of key frames extraction for sports videos

Wenjie Wang; Wei Li; Zhijun Song; Yuping Song; Jinhua Sun; Wenhui Gong; Hong Huang; Rongyu Yan

doi:10.1117/12.3051852

18 November 2024 An effective algorithm of key frames extraction for sports videos

Wenjie Wang, Wei Li, Zhijun Song, Yuping Song, Jinhua Sun, Wenhui Gong, Hong Huang, Rongyu Yan

Proceedings Volume 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) ; 134030O (2024) https://doi.org/10.1117/12.3051852
Event: International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, 2024, Zhengzhou, China

Abstract

This paper aims to extract keyframes and stage division of sports diving competition videos by advanced image processing and clustering algorithms to improve the efficiency of video analysis. Firstly, we cut the sports competition video into a series of images, store them in a folder, and perform Gaussian filtering and grayscale preprocessing. Then, a two-dimensional similarity matrix is constructed by comparing the Euclidean distance similarity of each frame with all other images in the cut order using a Siamese network. The positive characterization of the properties of the matrix is discussed in depth, which lays a theoretical foundation for the subsequent clustering analysis. An improved Jenks Natural Breaks clustering algorithm is used in the study to minimize intraclass variance and maximize inter-class separation. To optimize the clustering effect, this paper designs a new objective function, E, whose core idea is the exponent of the negative of the sum of interclass variance and intraclass variance. By comparing the values of E, we find the four-class clustering results that make the value of E more minor and thus identify the three keyframes in the video. These keyframes divide the video into four stages, effectively placing the change points between stages. Despite the interference of noisy points, the researcher further optimized the results through mean filtering processing. Ultimately, this approach improves video processing efficiency and lays the foundation for subsequent video evaluation and analysis through key frame extraction and stage division.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Wenjie Wang, Wei Li, Zhijun Song, Yuping Song, Jinhua Sun, Wenhui Gong, Hong Huang, and Rongyu Yan "An effective algorithm of key frames extraction for sports videos", Proc. SPIE 13403, International Conference on Algorithms, High Performance Computing, and Artificial Intelligence (AHPCAI 2024) , 134030O (18 November 2024); https://doi.org/10.1117/12.3051852

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KEYWORDS

Video

Video processing

Matrices

Tunable filters

Image processing

Gaussian filters

Feature extraction

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