New video semantic extraction method based on deep learning model

Abstract

The invention discloses a new video semantic extraction method based on a deep learning model. The new video semantic extraction method comprises the following steps: obtaining semantic structured video data by combining and segmenting a video frame sequence on the basis of a video physical structure; using a sliding window to process the semantic structured video data into the input data of a three-dimensional convolutional neural network; creating a three-dimensional convolutional neural network model, and using the output data of the sliding window as training data; using the output resultbased on the three-dimensional convolutional neural network as the input of the continuous time series classification algorithm, and completing the training of three-dimensional convolutional neural network parameters by the backpropagation algorithm; and using the trained three-dimensional convolutional neural network-continuous time series classification algorithm as a sports video semantic extraction model to extract video semantics. The proposed video semantic structuring method is combines with the three-dimensional convolutional neural network and the continuous time series classification algorithm, which can capture the connection between actions and improve the accuracy of sports video semantic extraction.

Description

technical field [0001] The invention relates to the technical fields of artificial intelligence and pattern recognition, in particular to a new video semantic extraction method based on a deep learning model. Background technique [0002] The concept of "semantics" originated at the end of the 19th century. It is the expression of the meanings represented by things in the real world corresponding to virtual data, and the relationship between these meanings. It is the interpretation and logical representation of virtual data in a certain field. . Moreover, "video semantics" is aimed at human thinking. When we want to use computers to understand the "semantics" in videos, computers can only recognize low-level features such as color and shape. Therefore, we need to use some methods to connect these low-level features to form some higher-level meanings, so as to better express the information to be displayed in the video. [0003] Video data is usually unstructured, so the se...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to overcome the existing technical deficiencies and provide a new video semantic extraction method based on a deep learning model, which uses a three-dimensional convolutional neural network model and a continuous time series classification algorithm to perform semantic Extraction, which can solve the problem of semantic analysis of sports videos

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