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Key Frame Extraction Algorithm of Surveillance Video Based on Quaternion Fourier Significance Detection

Zhang Yunzuo1,*, Zhang Jiayu1, Cai Zhaoquan2

1 School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang, 050043, Hebei, China
2 Shanwei Institute of Technology, Shanwei, 516600, Guangdong, China

* Corresponding Author: Zhang Yunzuo. Email: email

Journal of New Media 2022, 4(1), 1-11.


With the improvement of people's security awareness, numerous monitoring equipment has been put into use, resulting in the explosive growth of surveillance video data. Key frame extraction technology is a paramount technology for improving video storage efficiency and enhancing the accuracy of video retrieval. It can extract key frame sets that can express video content from massive videos. However, the existing key frame extraction algorithms of surveillance video still have deficiencies, such as the destruction of image information integrity and the inability to extract key frames accurately. To this end, this paper proposes a key frame extraction algorithm of surveillance video based on quaternion Fourier saliency detection. Firstly, the algorithm used colors, and intensity features to perform quaternion Fourier transform on surveillance video sequences. Next, the phase spectrum of the quaternion Fourier transformed image was obtained, and he image visual saliency map was obtained according to the quaternion Fourier phase spectrum. Then, the image visual saliency map of two adjacent frames is used to characterize the change of target motion state. Finally, the frames that can accurately express the motion state of the target are selected as key frames. The experimental results show that the method proposed in this paper can accurately capture the changes of the local motion state of the target while maintaining the integrity of the image information.


Cite This Article

Z. Yunzuo, Z. Jiayu and C. Zhaoquan, "Key frame extraction algorithm of surveillance video based on quaternion fourier significance detection," Journal of New Media, vol. 4, no.1, pp. 1–11, 2022.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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