LAUSR

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.