Paragraph association rule evaluation method based on multi-dimensional element video segmentation

Abstract

The invention mainly provides a paragraph association rule evaluation method based on multi-dimensional element video segmentation. The paragraph association rule evaluation method specifically comprises the steps of 1, carrying out video analysis; 2, extracting a key frame in scene segmentation; step 3, carrying out scene segmentation based on the key frame; step 4, carrying out video audio segmentation; step 5, performing semantic segmentation on the video; 6, judging the paragraph association rule of the segmented video of the GNN network; and step 7, constructing an association network. According to the method, after the same video segment is subjected to multi-dimensional segmentation, a paragraph association rule construction mode is adopted to carry out matching on corresponding multi-dimensional elements. Compard with other paragraph association rule evaluation methods for video segmentation, the video is well segmented in the image dimension by combining the change of the pixels in the image sequence in the time domain and the correlation between the adjacent frames, the key information of the video is reserved, and an effective multi-dimensional element video segmentationparagraph association rule judgment method can be provided.

Description

technical field [0001] The invention mainly relates to a method for judging paragraph association rules, in particular to a method for judging paragraph association rules based on multi-dimensional element video segmentation. Background technique [0002] At present, most of the video structuring problems are segmented on the single-dimensional element of the image, and the research on video structuring methods based on multi-dimensional segmentation is less involved. In practice, the audio information and text information contained in the video also play an important role in the video surveillance work. In addition, when segmenting and extracting key frames from moving objects in the video, in order to consider the problem of computational efficiency, only a certain frame in the video is taken as the key frame, and the important information contained in the video is often ignored, or by setting the threshold The above methods do not consider the changes of pixels in the im...

AI Technical Summary

Problems solved by technology

With the continuous deepening of edge computing, cloud computing, and big data technologies, problems such as huge video data, difficult storage, and inconvenient retrieval have become increasingly prominent. For large-scale real-time video surveillance data, it is necessary to perform real-time spatio-temporal information on video stream data. Labeling, character extraction, feature extraction, object classification, st

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