Prof. Yangjie Cao Profile

Prof. Yangjie Cao

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Proceedings Article | 14 December 2021 Paper

Research on real-time video key frame extraction technology based on clustering

Nan Lin, Chang Xu, Yinan Xu, Jianhong Ma, Yangjie Cao, Jie Li

Proceedings Volume 12087, 120872A (2021) https://doi.org/10.1117/12.2624849

KEYWORDS: Video, Detection and tracking algorithms, Video surveillance, Feature extraction, Cameras, Video compression, Data conversion, Neural networks

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A keyframe is a crucial image frame used to describe a shot, and the use of keyframe technology can significantly reduce the amount of data for video retrieval. For example,video-on-demand, face recognition under the camera, key lens retrieval of medical images, etc. Aiming at the problems in the current video keyframe extraction process that the extraction accuracy is low and cannot meet the real-time performance, this paper proposes a real-time video keyframe extraction algorithm CTM-NN based on the inter-frame difference method combined with clustering and neural network. The algorithm uses the inter-frame difference method based on the set threshold, HOG plus HSV first-order moment feature extraction algorithm, and uses the K-means++ clustering algorithm to finally train its own ResNet-50 model, aiming to accurately and efficiently extract real-time video Keyframes. In order to verify the algorithm proposed in this paper, experiments were carried out in the finished news video, landscape video, and real-time concrete mixing video. The experimental results show that the method proposed in this paper can meet the extraction accuracy and meet the keyframe extraction speed of the real-time video so that it can save the keyframes, automatically label while maintaining the time sequence. All in all, the CTM-NN algorithm proposed in this paper has achieved good results in the extraction and storage of real-time video keyframes

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