Video classification method and device based on deep learning

Abstract

The invention discloses a video classification method and device based on deep learning. The method comprises the following steps of acquiring a data set and a category label of the video; and equallydividing the obtained video into N segments, at the training stage, randomly selecting M frames for each video, stacking the video frames into a multi-channel image; and passing through a convolutional neural network, extracting video space weighted characteristics of the video divided into N segments, and passing through a convolutional neural network, extracting video time weighted characteristics of the video divided into N segments, and obtaining video multi-scale features of the video divided into the N segments, fusing the N segments of video features to calculate a prediction score, obtaining a final video classification, storing the trained video classification model, and classifying the test video by applying the trained video classification model. Through the mode, the effect and accuracy of video feature extraction can be improved.

Description

technical field [0001] The present invention relates to the field of video technology, in particular to a video classification method and equipment based on deep learning. Background technique [0002] With the continuous development of high-definition video equipment and other hardware equipment, artificial intelligence based on video classification technology is widely used in video interest recommendation, video security, smart home, etc., and the application scenarios are extremely broad. For the video providing platform, the video is classified, and when the user plays a certain video, similar videos can be recommended for the user, so as to improve the user's satisfaction with the video platform. For security companies, it is necessary to classify the abnormal behavior in the video and take corresponding security measures. For example, it can monitor the abnormal withdrawal of bank ATM (Automatic Teller Machine, automatic teller machine) cash machine and issue an alarm...

AI Technical Summary

Problems solved by technology

[0010] The process of artificially extracting video features in existing video classification schemes is mainly based on the geometric shape or outline of the video content subject, and the accuracy in some simple specific scenes is acceptable. However, in practical applications, very It is easily affected by many factors such as different lighting conditions, diverse viewing angles, complex backgrounds, and large intra-class changes, resulting in poor video feature extraction and average accuracy.

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