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Method, device, equipment and storage medium for video review

A video and historical video technology, applied in image communication, selective content distribution, electrical components, etc., can solve complex problems and achieve the effect of improving accuracy

Active Publication Date: 2021-06-15
BIGO TECH PTE LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] Embodiments of the present invention provide a method, device, device and storage medium for video auditing, so as to solve the problem of complex model training process in the prior art and improve the accuracy of video auditing

Method used

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  • Method, device, equipment and storage medium for video review
  • Method, device, equipment and storage medium for video review
  • Method, device, equipment and storage medium for video review

Examples

Experimental program
Comparison scheme
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Embodiment 1

[0031] Figure 1A A flow chart of a method of providing a video audit provided in the embodiment of the present invention, this embodiment can be applied to any video audit terminal that can be reviewed for Internet video that can be uploaded by the user. The technical solution of the embodiments of the present invention is applicable to how to accurately treat multi-class category categories of audit video for review. The method of the video audit provided in this embodiment can be performed by means of a video audit according to an embodiment of the present invention, which can be implemented by software and / or hardware, and integrated in an apparatus performed by the method. The device can be any intelligent terminal device carrying a corresponding video audit capability.

[0032] Specific, reference Figure 1A The method can include the steps of:

[0033] S110, extract the characteristic vector of the video to be reviewed.

[0034] Among them, the audio video refers to the us...

Embodiment 2

[0051] figure 2 The principle of the classification threshold determination process of the second classification model in the method provided in the second embodiment of the present invention is shown. This embodiment is further optimized on the basis of the above embodiment. Specifically, since the category score of the corresponding video category is greater than or equal to the corresponding classification threshold, it is necessary to send the information and the audit category to the manual audit platform to be reviewed. At this time, in order to ensure the send to the artificial review The number of platforms to be audited will not be too much, and the load capacity of auditors is exceeded, resulting in the problem of the backlog of the video in the human audit platform, and can also be determined according to the actual review of the actual audit platform. The corresponding classification threshold in the second classification model of the video category, which is controll...

Embodiment 3

[0069] Figure 3A with Figure 3B The schematic schematic of the second classification model of different video types in the method of the video audit provided in the first embodiment of the present invention. This embodiment is optimized on the basis of the above embodiment, and the specific training procedure of the second classification model of different video categories is mainly explained in this embodiment.

[0070] Optionally, in this embodiment, the following steps can be included:

[0071] S310, extract historical characteristic vectors of historical video, and determine the classification label of historical video.

[0072] Among them, history video has identified video of auditing categories in the actual audit business. Specifically, when training each two classification model, first, you need to obtain a large number of historical video that has already determined the video category. As a training sample, the model training is the sample, and the network is extracted ...

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Abstract

The invention discloses a video review method, device, equipment and storage medium, wherein the method includes: extracting a feature vector of a video to be reviewed; inputting the feature vectors of the video to be reviewed into binary classification models of different video categories, Obtain the classification score of the video to be reviewed under the corresponding video category; if the classification score of the video to be reviewed under the corresponding video category is greater than or equal to the corresponding classification threshold, the video category will be included as one of the review categories until the review of the video to be reviewed is obtained Category composition. In the technical solution provided by the embodiments of the present invention, since the video to be reviewed is classified by a plurality of binary classification models of different video categories, each binary classification model only judges a certain corresponding video category, compared with the scale of a single deep neural network model Smaller, the accuracy requirements for the classification ability are relatively low, which is convenient for training a more accurate classification model, thereby improving the accuracy of video review.

Description

Technical field [0001] Embodiments of the present invention relate to the field of content review, in particular, to a method, apparatus, device, and storage medium of a video audit. Background technique [0002] With the continuous development of Internet technology, especially the rise of mobile Internet and the advancement of 4G communication technologies and semiconductor technology, the amount of streaming information on the network has an explosion trend, so that all kinds of short video or network live broadcast on the Internet The content is getting stronger. At this time, the sensitive information and illegal information in Internet video also put forward higher audit requirements, which is difficult to effectively review all video data, and the artificial review must consume a lot of human cost, low efficiency. [0003] At present, existing video audit systems typically use a single depth neural network model to automatically review video content, although the video rev...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): H04N21/234H04N21/44
CPCH04N21/23418H04N21/44008
Inventor 潘跃刘振强邹昱梁柱锦
Owner BIGO TECH PTE LTD