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A video feature processing method and device

A technology of video features and processing methods, applied in the field of electronic information, to achieve the effect of improving accuracy

Active Publication Date: 2021-12-24
BEIJING QIYI CENTURY SCI & TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] This application provides a video feature processing method and device, the purpose of which is to solve the problem of how to fully extract the information in the video without consuming too much resources, so as to improve the accuracy of video classification

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  • A video feature processing method and device
  • A video feature processing method and device
  • A video feature processing method and device

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Embodiment Construction

[0045] The inventors found that when the three-dimensional convolutional neural network model extracts video features, the effect of the time-domain features extracted by it is uneven for different videos. After further research, it was found that the effect is directly related to the length of the video. For some moderately long videos, the effect is better, but for some short or long videos, the temporal feature effect is relatively poor. The reason is that for a convolutional layer with the same convolution kernel expansion coefficient, when the video duration is short, it is difficult to capture a certain frame of image that has changed, thus missing key information, resulting in poor temporal feature effects , and when the video duration was long at that time, if a certain action lasted for several seconds, it was difficult to capture the information when the action changed in a timely and effective manner.

[0046] In order to solve the above problems, the embodiment of...

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Abstract

The present application discloses a video feature processing method and device, including: obtaining the video feature vector obtained after three-dimensional convolution processing; performing convolution processing on the video feature vector in the spatial domain to obtain the spatial domain processing result; dividing the spatial domain processing result into multiple groups Spatial domain processing sub-results, and at least two groups of spatial domain processing sub-results are convolved in the time domain to obtain at least two groups of time-domain processing sub-results, wherein the convolution of each group of spatial domain processing sub-results in the time domain The expansion coefficients of the processed convolution kernels are different; at least two groups of time-domain processing sub-results are spliced ​​to obtain the processed video feature vector. In the method disclosed in this application, each group of spatial domain processing sub-results is convolved with different convolution kernel expansion coefficients, that is, multiple scales perform temporal convolution processing on video features, which can capture more timely and comprehensive The information of the image at the moment when the change occurs can improve the accuracy of the temporal feature.

Description

technical field [0001] The present application relates to the field of electronic information, in particular to a video feature processing method, device and three-dimensional convolutional neural network model. Background technique [0002] Video feature extraction is a basic link in video processing. In almost all video analysis and processing processes, video features need to be extracted first. The three-dimensional convolutional neural network model is widely used because it can better capture the characteristic information of time domain and space domain at the same time. The process of using the three-dimensional convolutional neural network model for video feature extraction is actually to perform convolution operations on the time and space dimensions at the same time, so as to obtain the visual features of each frame of the video in the video. The correlation of adjacent image frames over time. [0003] However, when the current 3D convolutional neural network mo...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/00G06N3/04G06N3/08G06T3/40G06T5/30
CPCG06T3/4038G06T5/30G06N3/08G06T2207/10016G06T2200/32G06V20/46G06N3/045
Inventor 张云桃晋瑞锦
Owner BEIJING QIYI CENTURY SCI & TECH CO LTD