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Duplicate video recognition method and device, terminal, and computer-readable storage medium

A video recognition and video technology, applied in the computer field, can solve problems such as inability to meet large-scale commercialization, inability to adapt to large-scale video repeated detection, and coarse calculation granularity, and achieves a high recall rate, an increase in accuracy rate, and an improvement in time efficiency. Effect

Active Publication Date: 2019-01-11
BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

The calculation granularity of this method is rough, and it needs to perform two-two cross calculation for all videos. The time consumption increases exponentially with the increase of the number of videos. It cannot adapt to the problem of repeated detection of large-scale videos, nor can it meet the needs of large-scale commercialization.

Method used

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  • Duplicate video recognition method and device, terminal, and computer-readable storage medium
  • Duplicate video recognition method and device, terminal, and computer-readable storage medium
  • Duplicate video recognition method and device, terminal, and computer-readable storage medium

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0058] In a specific embodiment, a method for identifying repetitive videos is provided, such as figure 1 and figure 2 shown, including:

[0059] Step S100: Extract key frames of the video to be recognized.

[0060] First, the video to be recognized is finely cut into frames and divided into consecutive frames. Then, using the key frame extraction method based on image mutation, select the frame whose image content change is greater than the mutation threshold in consecutive frames as the key frame of the video to be recognized. For example, the first key frame, the second key frame, the third key frame, etc. are extracted. Finally, all the key frames extracted form the key frame sequence of the video to be recognized. It should be noted that the time sequence of the video to be recognized can be divided into a plurality of continuously distributed time intervals, and one frame is extracted from each time interval as a key frame. For example, in a time interval of 5 seco...

Embodiment 2

[0092] In another specific embodiment, a repeat video identification device is provided, such as Figure 4 shown, including:

[0093] Key frame extraction module 10, for extracting the key frame of video to be identified;

[0094] The visual feature extraction module 20 is used to extract a visual feature vector according to the key frame of the video to be identified, and the visual feature vector includes a plurality of visual feature values;

[0095] The key frame retrieval module 30 is used to obtain the key frame of the stored video corresponding to the visual feature value from the key frame image retrieval library;

[0096] A similarity calculation module 40, configured to calculate the video similarity between the video to be identified and the stored video according to the key frame of the video to be identified and the key frame of the stored video;

[0097] A duplicate video judging module 50, configured to confirm whether the video to be identified and the stored...

Embodiment 3

[0112] An embodiment of the present invention provides a repetitive video identification terminal, such as Figure 5 shown, including:

[0113] A memory 400 and a processor 500 , the memory 400 stores computer programs that can run on the processor 500 . When the processor 500 executes the computer program, it implements the repeated video identification method in the foregoing embodiments. The number of memory 400 and processor 500 may be one or more.

[0114] The communication interface 600 is used for the memory 400 and the processor 500 to communicate with the outside.

[0115] The memory 400 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one magnetic disk memory.

[0116] If the memory 400, the processor 500, and the communication interface 600 are implemented independently, the memory 400, the processor 500, and the communication interface 600 may be connected to each other through a bus to compl...

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Abstract

The invention provides a duplicate video identification method and device and a terminal. The method comprises the following steps: extracting a key frame of a video to be identified; extracting a visual feature vector according to a key frame of the video to be recognized, wherein the visual feature vector comprises a plurality of visual eigenvalues; obtaining a key frame of a stored video corresponding to a visual eigenvalue from a key frame image retrieval database; calculating a video similarity between the video to be identified and the stored video according to the key frame of the videoto be identified and the key frame of the stored video; judging whether the video to be identified and the stored video are duplicate video according to the video similarity. The method avoids the tedious process of calculating the similarity of key frame sequences in pairs for a large number of videos, improves the time efficiency of video repetition detection, and improves the recall rate and accuracy of video repetition detection.

Description

technical field [0001] The present invention relates to the field of computer technology, in particular to a method, device and terminal for identifying repeated videos. Background technique [0002] In traditional video repetition identification methods, the similarity between videos is usually determined by calculating the similarity of key frames arranged in time order between videos, so as to determine whether the videos are repeated. However, the calculation of key frame similarity often uses direct comparison of whether the signatures are equal, or uses multiple high-dimensional vectors to mark an image from different dimensions, and then reduces the dimensionality and maps it into a fixed-length string for pairwise comparison. At this time, the distance between the strings is calculated, and combined with the threshold, to determine whether the two images are similar. The calculation granularity of this method is rough, and pairwise cross calculation is required for ...

Claims

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Application Information

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IPC IPC(8): G06F16/78G06K9/62
CPCG06F18/22
Inventor 李帅龙宋萌萌谭洪林李棱
Owner BAIDU ONLINE NETWORK TECH (BEIJIBG) CO LTD
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