A Novel 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, which includes the following steps: based on the video physical structure, by combining and segmenting video frame sequences, semantically structured video data is obtained; by using a sliding window to extract semantically The structured video data is processed into the input data of the three-dimensional convolutional neural network; the three-dimensional convolutional neural network model is created, and the output data of the sliding window is used as the training data; the output result of the three-dimensional convolutional neural network is used as the input of the continuous time series classification algorithm , complete the training of the parameters of the 3D convolutional neural network through the backpropagation algorithm; use the trained 3D convolutional neural network-continuous time series classification algorithm as the sports video semantic extraction model to extract video semantics. The present invention combines the three-dimensional convolutional neural network and the continuous sequence classification algorithm through the proposed video semantic structuring method to better 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...

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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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