Key Frame Extraction Algorithm of Motion Video Based on Priori

Qi Zhong,Yuan Zhang,Yang Yu,Jinguo Zhang,Chang Liu,Kaixuan Shi

doi:10.1109/access.2020.3025774

Abstract

Key frame extraction technology is one of the core technologies of content-based video retrieval. For video types with complex content, various scenes, and rich actions, the performance of existing key frame extraction methods is not ideal. Based on the Visual Geometry Group (VGG), this article proposes an image saliency extraction model assisted by deep prior information, and uses a large-scale data set for training on the server to obtain a trained model, and then integrates multiple features. The saliency extraction algorithm is combined with the image saliency extraction model assisted by deep prior information, and a saliency extraction algorithm based on multi-feature fusion and deep prior information is proposed. A new method for extracting key frames of motion video is introduced in detail. Taking into account that sports videos in real applications are susceptible to interference from various factors, resulting in poor picture quality, this article constructs a new visual attention model for moving targets in sports videos, which integrates images. The combination of multiple features of the bottom-level features and the skin color confidence map of the moving target overcomes the problem that a single feature cannot fully express the moving target. Since the processing object in this article is for the moving target in the video of the sports room, the extracted moving target can provide samples for video post-processing. The experimental results show that the proposed key frame extraction algorithm can quickly grasp the pedestrian information in the motion video and provide effective processing samples for the motion target for video post-processing.

Highlights

With the further development of Internet technology and multimedia technology, as well as the popularization of digital devices and large-capacity storage devices, a large amount of video information is generated every day [1], [2]
MOTION VIDEO DATA In order to analyze the specific situation of wrong matching, Figure 7 shows an example of the wrong matching moving target in case video 1
According to the key frame extraction algorithm in this article, the algorithm in this article is sensitive to changes in the moving face area, so it can obtain the frame with a large proportion of the moving target face area in the picture as the key frame

Summary

Introduction

With the further development of Internet technology and multimedia technology, as well as the popularization of digital devices and large-capacity storage devices, a large amount of video information is generated every day [1], [2]. Video information is widely used in all areas of our lives. People hope that the retrieval, browsing, and storage of videos can be as efficient as text data, and they can get what they are interested in through quick browsing [3]–[5]. The video data is much more complex than text and text data, and the content is rich and changeable, which brings great difficulties to subsequent processing [6]. The variety of video content and formats on the Internet makes it an urgent problem to quickly and effectively find content of interest

Methods

Results

Conclusion