CN114519828A - Video detection method and system based on semantic analysis - Google Patents
Video detection method and system based on semantic analysis Download PDFInfo
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- CN114519828A CN114519828A CN202210045862.0A CN202210045862A CN114519828A CN 114519828 A CN114519828 A CN 114519828A CN 202210045862 A CN202210045862 A CN 202210045862A CN 114519828 A CN114519828 A CN 114519828A
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Abstract
The invention provides a video detection method and system based on semantic analysis, which are used for obtaining image jumping points by calculating a characteristic value of a histogram of each frame in the gradient direction, labeling by using a label, inputting a frame image at the later moment of the label into a semantic analysis model and a graphic analysis model in parallel, judging whether the frame image is in compliance, and further judging whether a segmented video data stream is in compliance.
Description
Technical Field
The present application relates to the field of network multimedia, and in particular, to a method and system for video detection based on semantic analysis.
Background
With the rapid development of networks, a large number of video programs appear, and the amateur life of people is enriched. However, there is a problem that content that is not compliant such as violence may appear in the video, which is a risk of harming the society. In the face of non-compliant video, since the network propagation speed is very fast, it is required to be able to quickly identify and efficiently process. Meanwhile, in the prior art, most of the methods are character and image identification methods, and a video identification method is lacked.
Therefore, a method and a system for targeted semantic analysis-based video detection are urgently needed.
Disclosure of Invention
The invention aims to provide a video detection method and system based on semantic analysis, which are characterized in that image jumping points are obtained by calculating a characteristic value of a histogram of each frame in the gradient direction, labels are used for labeling, a frame image at the moment after the labels is input into a semantic analysis model and a graphic analysis model in parallel, whether the frame image is in compliance is judged, and whether segmented video data streams are in compliance is further judged; the method can quickly identify and effectively process the video images which are not in compliance.
In a first aspect, the present application provides a method for video detection based on semantic analysis, where the method includes:
acquiring a video data stream, calculating a characteristic value of a histogram of each frame in a gradient direction, judging that image skipping occurs between the frames when the difference of the characteristic values between the frames is larger than a preset threshold value, and inserting a label between the frames, wherein the label is used for marking a point of the image skipping;
extracting a frame image at the next moment after the label according to the label, inputting the frame image into a semantic analysis model, analyzing character information contained in the frame image, acquiring key character features, judging whether the frame image comprises non-compliant character content or not, and obtaining a first judgment result;
inputting the frame image of the later moment of the label into a graph analysis model in parallel, identifying object information contained in the frame image, acquiring key object characteristics, judging whether the frame image comprises non-compliant graph content or not, and obtaining a second judgment result;
determining whether the frame image at the later moment of the label is in compliance according to the first judgment result and the second judgment result, if so, judging a video data stream between the current label and the next label as a compliant video data stream, and storing the compliant video data stream in a server; otherwise, judging that a section of video data stream between the current label and the next label is not compliant, and deleting the section of video data stream;
and moving to the next label, and repeating the action of extracting the frame image at the moment after the label until all the video data streams are judged to be finished.
With reference to the first aspect, in a first possible implementation manner of the first aspect, the detecting a change in a grayscale centroid position of the image includes detecting a change in a feature value of the histogram in a gradient direction.
With reference to the first aspect, in a second possible implementation manner of the first aspect, the acquiring a video data stream includes acquiring videos from multiple different platform sources, and encoding and decoding the video data stream.
With reference to the first aspect, in a third possible implementation manner of the first aspect, the kernels of the semantic analysis model and the graphical analysis model both use a neural network model.
In a second aspect, the present application provides a system for video detection based on semantic analysis, the system comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method of any one of the four possibilities of the first aspect according to instructions in the program code.
In a third aspect, the present application provides a computer readable storage medium for storing program code for performing the method of any one of the four possibilities of the first aspect.
The invention provides a video detection method and system based on semantic analysis, which are characterized in that image jumping points are obtained by calculating a characteristic value of a histogram of each frame in the gradient direction, labels are used for labeling, a frame image at the later moment of the label is input into a semantic analysis model and a graphic analysis model in parallel, whether the frame image is in compliance is judged, and whether segmented video data streams are in compliance is further judged.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described in detail below with reference to the accompanying drawings so that the advantages and features of the present invention can be more easily understood by those skilled in the art, and the scope of the present invention will be more clearly and clearly defined.
Fig. 1 is a flowchart of a video detection method based on semantic analysis provided in the present application, including:
acquiring a video data stream, calculating a characteristic value of a histogram of each frame in a gradient direction, judging that image skipping occurs between the frames when the difference of the characteristic values between the frames is larger than a preset threshold value, and inserting a label between the frames, wherein the label is used for marking a point of the image skipping;
extracting a frame image at the next moment after the label according to the label, inputting the frame image into a semantic analysis model, analyzing character information contained in the frame image, acquiring key character features, judging whether the frame image comprises non-compliant character content or not, and obtaining a first judgment result;
inputting the frame image of the later moment of the label into a graph analysis model in parallel, identifying object information contained in the frame image, acquiring key object characteristics, judging whether the frame image comprises non-compliant graph content or not, and obtaining a second judgment result;
determining whether the frame image at the later moment of the label is in compliance according to the first judgment result and the second judgment result, if so, judging a video data stream between the current label and the next label as a compliant video data stream, and storing the compliant video data stream in a server; otherwise, judging that a section of video data stream between the current label and the next label is not compliant, and deleting the section of video data stream;
and moving to the next label, and repeating the action of extracting the frame image at the moment after the label until all the video data streams are judged to be finished.
In some preferred embodiments, the feature value of the histogram in the gradient direction includes detecting a change in a location of a grayscale centroid of the image.
In some preferred embodiments, the acquiring the video data stream includes acquiring videos of a plurality of different platform sources, and encoding and decoding the video data stream.
In some preferred embodiments, the kernels of the semantic analysis model and the graphical analysis model both use a neural network model.
The application provides a video detection system based on semantic analysis, the system includes: the system includes a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method according to any of the embodiments of the first aspect according to instructions in the program code.
The present application provides a computer readable storage medium for storing program code for performing the method of any of the embodiments of the first aspect.
In specific implementation, the present invention further provides a computer storage medium, where the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments of the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The same and similar parts between the various embodiments of the present specification may be referred to each other. In particular, for the embodiments, since they are substantially similar to the method embodiments, the description is simple, and the relevant points can be referred to the description in the method embodiments.
The above-described embodiments of the present invention do not limit the scope of the present invention.
Claims (6)
1. A video detection method based on semantic analysis is characterized by comprising the following steps:
acquiring a video data stream, calculating a characteristic value of a histogram of each frame in a gradient direction, judging that image skipping occurs between the frames when the difference of the characteristic values between the frames is larger than a preset threshold value, and inserting a label between the frames, wherein the label is used for marking a point of the image skipping;
extracting a frame image at the next moment after the label according to the label, inputting the frame image into a semantic analysis model, analyzing character information contained in the frame image, acquiring key character features, judging whether the frame image comprises non-compliant character content or not, and obtaining a first judgment result;
inputting the frame image of the later moment of the label into a graph analysis model in parallel, identifying object information contained in the frame image, acquiring key object characteristics, judging whether the frame image comprises non-compliant graph content or not, and obtaining a second judgment result;
determining whether the frame image at the later moment of the label is in compliance according to the first judgment result and the second judgment result, if so, judging a video data stream between the current label and the next label as a compliant video data stream, and storing the compliant video data stream in a server; otherwise, judging that a section of video data stream between the current label and the next label is not compliant, and deleting the section of video data stream;
and moving to the next label, and repeating the action of extracting the frame image at the moment after the label until all the video data streams are judged to be finished.
2. The semantic analysis based video detection method according to claim 1, characterized in that: the feature value of the histogram in the gradient direction includes detecting a change in a gray centroid position of the image.
3. The video detection method based on semantic analysis according to any one of claims 1-2, characterized in that: the acquiring the video data stream comprises acquiring videos of a plurality of different platform sources and encoding and decoding the video data stream.
4. The semantic analysis based video detection method according to any one of claim 3, characterized in that: the kernels of the semantic analysis model and the graphic analysis model both use a neural network model.
5. A video detection system based on semantic analysis, the system comprising a processor and a memory:
the memory is used for storing program codes and transmitting the program codes to the processor;
the processor is configured to perform the method according to instructions in the program code to implement any of claims 1-4.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium is configured to store a program code for performing an implementation of the method of any of claims 1-4.
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| KR20030067135A (en) * | 2002-02-07 | 2003-08-14 | (주)지토 | Internet broadcasting system using a content based automatic video parsing |
| CN110647804A (en) * | 2019-08-09 | 2020-01-03 | 中国传媒大学 | Violent video identification method, computer system and storage medium |
| CN112989950A (en) * | 2021-02-11 | 2021-06-18 | 温州大学 | Violent video recognition system oriented to multi-mode feature semantic correlation features |
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- 2022-01-17 CN CN202210045862.0A patent/CN114519828A/en active Pending
Patent Citations (3)
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|---|---|---|---|---|
| KR20030067135A (en) * | 2002-02-07 | 2003-08-14 | (주)지토 | Internet broadcasting system using a content based automatic video parsing |
| CN110647804A (en) * | 2019-08-09 | 2020-01-03 | 中国传媒大学 | Violent video identification method, computer system and storage medium |
| CN112989950A (en) * | 2021-02-11 | 2021-06-18 | 温州大学 | Violent video recognition system oriented to multi-mode feature semantic correlation features |
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| 刘秀彬: "《网络视频监控技术》", 中国传媒大学出版社, pages: 194 - 197 * |
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