CN116389855A - Video tagging method based on OCR - Google Patents
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- 238000004458 analytical method Methods 0.000 claims abstract description 43
- 239000013598 vector Substances 0.000 claims description 21
- 238000004422 calculation algorithm Methods 0.000 claims description 6
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000010835 comparative analysis Methods 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000007499 fusion processing Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 238000007619 statistical method Methods 0.000 claims description 3
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/84—Generation or processing of descriptive data, e.g. content descriptors
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- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/70—Information retrieval; Database structures therefor; File system structures therefor of video data
- G06F16/78—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually
- G06F16/7867—Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually using information manually generated, e.g. tags, keywords, comments, title and artist information, manually generated time, location and usage information, user ratings
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/28—Quantising the image, e.g. histogram thresholding for discrimination between background and foreground patterns
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/46—Extracting features or characteristics from the video content, e.g. video fingerprints, representative shots or key frames
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/49—Segmenting video sequences, i.e. computational techniques such as parsing or cutting the sequence, low-level clustering or determining units such as shots or scenes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/43—Processing of content or additional data, e.g. demultiplexing additional data from a digital video stream; Elementary client operations, e.g. monitoring of home network or synchronising decoder's clock; Client middleware
- H04N21/44—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream, rendering scenes according to MPEG-4 scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- H—ELECTRICITY
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- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/84—Generation or processing of descriptive data, e.g. content descriptors
- H04N21/8405—Generation or processing of descriptive data, e.g. content descriptors represented by keywords
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- H—ELECTRICITY
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/80—Generation or processing of content or additional data by content creator independently of the distribution process; Content per se
- H04N21/83—Generation or processing of protective or descriptive data associated with content; Content structuring
- H04N21/845—Structuring of content, e.g. decomposing content into time segments
- H04N21/8456—Structuring of content, e.g. decomposing content into time segments by decomposing the content in the time domain, e.g. in time segments
Abstract
The invention discloses a video tag method based on OCR, which is used for respectively analyzing an input field command video and a radar video, defining the field command video as a left path video, defining the field radar video as a right path video, carrying out segment analysis on the field command video and the radar video, setting the interval of each segment of video through front end configuration, generating a video tag at each time interval, and obtaining the uniqueness of the video tag through analysis on historical interval data records. The method comprises the following specific steps: processing left path video: step one, continuously acquiring a video frame sequence, wherein each time interval T1, T1 is more than or equal to 1min, and setting the video frame sequence as a video analysis unit to obtain S video analysis units. When the method is implemented, each video unit obtains the unique label, and the purposes of quickly previewing the key event and saving more storage space are achieved.
Description
Technical Field
The invention belongs to the technical field of intelligent video analysis, and particularly relates to a video tag method based on OCR.
Background
Video tagging is a specific phrase used to describe video features, and tagging video can help users to quickly and efficiently retrieve video content. The existing video tag generation method mainly relies on manual marking, and for the online video tag generation method, the online video tag generation method mainly starts based on the aspects of image, video or voice text understanding and the like. From the image perspective, frames are mainly extracted from a video to obtain a picture, the picture is marked, and finally, image tags of the video are integrated to obtain a video tag. From the video perspective, the video label is mainly obtained by using a video understanding method.
In actual command and combat, a command video image and a radar video image are usually synthesized into one path of video through a video acquisition device, and a large amount of useless information exists in the video, so that the synthesized video data needs to be subjected to labeling treatment, and more useful video information is reserved.
Disclosure of Invention
Accordingly, the present invention has been made to solve the above-mentioned problems occurring in the prior art, by providing an OCR-based video tagging method.
In order to achieve the above object, the present invention provides the following technical solutions:
according to the video tagging method based on OCR, the input site command video and radar video are respectively analyzed, the site command video is defined as a left-way video, the site radar video is defined as a right-way video, the site command video and the radar video are subjected to segmentation analysis, the interval of each video can be set through front end configuration, a video tag is generated at each time interval, and the uniqueness of the video tag is obtained through analysis of historical interval data records.
Further, the specific steps are as follows:
processing left path video:
step one, a sequence of video frames is continuously acquired, with each time interval T1,
setting the video analysis unit as one video analysis unit to obtain S video analysis units;
in the Si-th video analysis unit, taking a frame of image Ik every N seconds to extract edge characteristics, wherein the Si-th video analysis unit can obtain T60/N image characteristics containing edge information; the method comprises the following steps: firstly, carrying out graying treatment on an image Ik, and converting a color image into a gray image Gk; extracting the edges of the gray level image Gk, wherein the edge extraction algorithm can firstly obtain a binarization characteristic image by using conventional algorithms such as sobel, canny and the like in the prior art;
step three, carrying out normalization processing on each image feature containing edge information: the method comprises the following steps: counting the number of the binarized feature points and recording as
The characteristic value is normalized to be a function of the characteristic value,
where w represents the image width and h represents the image height, resulting in a set of T1 x 60/N-dimensional vector arrays si ；
Step three, each video unit is analyzed, and the current Arrayl is utilized si Vector and history ArrayL H Each vector in the queue performs Euclidean distance calculation, if the distance is greater than a threshold T, flag=0 is set, flag=1 is set, and the array is simultaneously used si Adding to history queue ArrayL H And the judgment basis of the next round.
Further, the specific steps are as follows:
right-way video processing:
s1, continuously collecting a right-path video frame sequence, T2,
obtaining M right-path video analysis units for one video analysis unit;
s2, in a Mi video analysis unit, taking a frame of image Pk every N seconds to obtain T2 x 60/N pieces of image data to be analyzed;
s3, T1 x 60/N-dimensional vector array based on left-path video analysis s The RN vectors in the samples are recorded as
For ArrayR N The vectors are ordered from big to small;
s4, selecting the first Q ArrayRs N OCR recognition is carried out on the image corresponding to the vector value;
s5, merging the recognition results based on a statistical method for the Q recognition results obtained by recognition, namely, performing comparative analysis on the recognition results of the OCR at the corresponding positions, and recording the statistical results in a form of a table;
s6, comparing the historical OCR information with the historical OCR information, if the similarity is larger than a threshold D, setting flagR=0, and setting the similarity smaller than or equal to D, setting flagR=1, and simultaneously adding the current OCR recognition information into a historical information queue to be used as a judgment basis of the next round.
Further, the specific steps are as follows,
and (3) data fusion processing:
the result value of each set video unit is obtained through the analysis of the left path video and the right path video, the next analysis is carried out according to the result value, and the method is concretely realized as follows,
obtaining state values of a left video flag L and a right video flag R;
performing AND operation on the status values of the flag L and the flag R, and when the status values are 1, indicating that the video tag belongs to a unique tag;
and combining the character information into a character string by utilizing unique tag information obtained by OCR, and then renaming the video unit to generate a tagged video file.
Further, the interval of each video may be 5 minutes, 10 minutes, or 15 minutes.
The invention has the following advantages:
when the method is implemented, each video unit obtains the unique label, and the purposes of quickly previewing the key event and saving more storage space are achieved.
Drawings
Fig. 1 is a flow chart of an OCR-based video tagging method provided in some embodiments of the present invention.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, in the video tagging method based on OCR according to the embodiment of the first aspect of the present invention, an input field command video and a radar video are respectively analyzed, the field command video is defined as a left path video, the field radar video is defined as a right path video, for more precisely tagging the two paths of videos, the video is segmented and analyzed, and an interval of each segment of video can be set through front end configuration, and the time interval is generally 5 minutes, 10 minutes and 15 minutes, that is, each time interval generates a video tag, and the uniqueness of the video tag is obtained through analysis of historical interval data records, which specifically includes the following steps:
left path video processing:
a sequence of video frames is continuously acquired, with each interval T1,
setting the video analysis unit as one video analysis unit to obtain S video analysis units;
in the Si-th video analysis unit, taking a frame of image Ik every N seconds to extract edge characteristics, wherein the Si-th video analysis unit can obtain T1 x 60/N image characteristics containing edge information; the method comprises the following steps: firstly, carrying out graying treatment on an image Ik, and converting a color image into a gray image Gk; extracting the edges of the gray level image Gk, wherein the edge extraction algorithm can firstly obtain a binarization characteristic image by using conventional algorithms such as sobel, canny and the like in the prior art;
normalization processing is carried out on each image feature containing edge information: the method comprises the following steps: counting the number of the binarized feature points and recording as
The characteristic value is normalized to be a function of the characteristic value,
where w represents the image width and h represents the image height, resulting in a set of T60/N-dimensional vectors Arrayl si ；
Analyzing each video unit using the current Arrayl si Vector and history ArrayL H Each vector in the queue performs Euclidean distance calculation, if the distance is greater than a threshold T, flag=0 is set, flag=1 is set, and the array is simultaneously used si Adding to history queue ArrayL H And the judgment basis of the next round.
Right-way video processing:
the right video frame sequence, T2,
obtaining M right-path video analysis units for one video analysis unit;
in a Mi video analysis unit, taking a frame of image Pk every N seconds to obtain T2 x 60/N pieces of image data to be analyzed;
t1 x 60/N-dimensional vector Arrayl based on left-path video analysis si The RN vectors in the samples are recorded as
For ArrayR N The vectors are ordered from big to small;
selecting the first Q ArrayRs N OCR recognition is carried out on the image corresponding to the vector value;
the Q OCR recognition results obtained by recognition are combined based on a statistical method, and the recognition results are specifically: performing comparative analysis on OCR recognition results of corresponding positions, and recording statistical results in a form of a table;
comparing with the historical OCR information, if the similarity is larger than a threshold value D, setting FlagR=0, and setting FlagR=1, and meanwhile adding the current OCR recognition information into a historical information queue to be used as a judgment basis of the next round.
And (3) data fusion processing:
the result value of each set video unit is obtained through the analysis of the left path video and the right path video, and the next analysis is carried out according to the result value, and the specific implementation is as follows:
obtaining state values of a left video flag L and a right video flag R;
performing AND operation on the status values of the flag L and the flag R, and when the status values are 1, indicating that the video tag belongs to a unique tag;
and combining the character information into a character string by utilizing unique tag information obtained by OCR, and renaming the video unit to generate a tagged video file.
Standard parts used in the invention can be purchased from the market, special-shaped parts can be customized according to the description of the specification and the drawings, the specific connection modes of all parts adopt conventional means such as mature bolts, rivets and welding in the prior art, the machinery, the parts and the equipment adopt conventional modes in the prior art, and the circuit connection adopts conventional connection modes in the prior art, so that details are not described in detail in the specification, and the invention belongs to the prior art known to the person skilled in the art.
Although the present invention has been described with reference to the foregoing embodiments, it will be apparent to those skilled in the art that modifications may be made to the embodiments described, or equivalents may be substituted for elements thereof, and any modifications, equivalents, improvements and changes may be made without departing from the spirit and principles of the present invention.
Claims (5)
1. The video tag method based on OCR is characterized in that the input site command video and radar video are respectively analyzed, the site command video is defined as a left path video, the site radar video is defined as a right path video, the site command video and the radar video are subjected to segmentation analysis, the interval of each segment of video can be set through front end configuration, a video tag is generated at each time interval, and the uniqueness of the video tag is obtained through analysis of historical interval data records.
2. The OCR based video tagging method of claim 1, comprising the specific steps of:
processing left path video:
step one, a sequence of video frames is continuously acquired, with each time interval T1,
in the Si-th video analysis unit, taking a frame of image Ik every N seconds to extract edge characteristics, wherein the Si-th video analysis unit can obtain T60/N image characteristics containing edge information; the method comprises the following steps: firstly, carrying out graying treatment on an image Ik, and converting a color image into a gray image Gk; extracting the edges of the gray level image Gk, wherein the edge extraction algorithm can firstly obtain a binarization characteristic image by using conventional algorithms such as sobel, canny and the like in the prior art;
step three, carrying out normalization processing on each image feature containing edge information: the method comprises the following steps: counting the number of the binarized feature points and recording as
Step three, each video unit is analyzed, and the current Arrayl is utilized si Vector and history ArrayL H Each vector in the queue performs Euclidean distance calculation, if the distance is greater than a threshold T, flag=0 is set, flag=1 is set, and the array is simultaneously used si Adding to history queue ArrayL H And the judgment basis of the next round.
3. The OCR based video tagging method of claim 2, comprising the specific steps of:
right-way video processing:
s1, continuously collecting a right-path video frame sequence, T2,
s2, in a Mi video analysis unit, taking a frame of image Pk every N seconds to obtain T2 x 60/N pieces of image data to be analyzed;
s3, T1 x 60/N-dimensional vector array based on left-path video analysis s The RN vectors in the samples are recorded as
s4, selecting the first Q ArrayRs N OCR recognition is carried out on the image corresponding to the vector value;
s5, merging the recognition results based on a statistical method for the Q recognition results obtained by recognition, namely, performing comparative analysis on the recognition results of the OCR at the corresponding positions, and recording the statistical results in a form of a table;
s6, comparing the historical OCR information with the historical OCR information, if the similarity is larger than a threshold D, setting flagR=0, and setting the similarity smaller than or equal to D, setting flagR=1, and simultaneously adding the current OCR recognition information into a historical information queue to be used as a judgment basis of the next round.
4. The method for video tagging based on OCR according to claim 3, comprising the steps of,
and (3) data fusion processing:
the result value of each set video unit is obtained through the analysis of the left path video and the right path video, the next analysis is carried out according to the result value, and the method is concretely realized as follows,
obtaining state values of a left video flag L and a right video flag R;
performing AND operation on the status values of the flag L and the flag R, and when the status values are 1, indicating that the video tag belongs to a unique tag;
and combining the character information into a character string by utilizing unique tag information obtained by OCR, and then renaming the video unit to generate a tagged video file.
5. The OCR based video tagging method of claim 1, wherein the interval of each video segment may be 5 minutes, 10 minutes, or 15 minutes.
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| CN113534146A (en) * | 2021-07-26 | 2021-10-22 | 中国人民解放军海军航空大学 | Radar video image target automatic detection method and system |
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- 2023-06-01 CN CN202310639256.6A patent/CN116389855A/en active Pending
Patent Citations (6)
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| CN102663391A (en) * | 2012-02-27 | 2012-09-12 | 安科智慧城市技术（中国）有限公司 | Image multifeature extraction and fusion method and system |
| CN113534146A (en) * | 2021-07-26 | 2021-10-22 | 中国人民解放军海军航空大学 | Radar video image target automatic detection method and system |
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