CN107707975A - Video intelligent clipping method based on monitor supervision platform - Google Patents
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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 or rendering scenes according to encoded video stream scene graphs
- H04N21/44008—Processing of video elementary streams, e.g. splicing a video clip retrieved from local storage with an incoming video stream or rendering scenes according to encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics in the video stream
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/40—Scenes; Scene-specific elements in video content
- G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
- G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/20—Servers specifically adapted for the distribution of content, e.g. VOD servers; Operations thereof
- H04N21/23—Processing of content or additional data; Elementary server operations; Server middleware
- H04N21/234—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs
- H04N21/23418—Processing of video elementary streams, e.g. splicing of video streams or manipulating encoded video stream scene graphs involving operations for analysing video streams, e.g. detecting features or characteristics
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- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N21/00—Selective content distribution, e.g. interactive television or video on demand [VOD]
- H04N21/40—Client devices specifically adapted for the reception of or interaction with content, e.g. set-top-box [STB]; Operations thereof
- H04N21/47—End-user applications
- H04N21/472—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content
- H04N21/47205—End-user interface for requesting content, additional data or services; End-user interface for interacting with content, e.g. for content reservation or setting reminders, for requesting event notification, for manipulating displayed content for manipulating displayed content, e.g. interacting with MPEG-4 objects, editing locally
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
- H04N7/00—Television systems
- H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
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Abstract
The present invention relates to a kind of video intelligent clipping method based on monitor supervision platform, including：Sport foreground, and the sport foreground point number in statistics frame are extracted, carries out threshold process；Extract sport foreground profile and carry out human testing；To the doubtful frame of video containing personnel, its quantity is counted, if the frame number after statistics is very few, video clipping processing below is no longer carried out, saves as former video；Conversely, then continuing each video frame images of multiple dimensioned scanning input, it is detected and judged with the human testing assembled classifier of pre-training, will be deemed as the frame of video comprising human body and be defined as key frame of video；The key frame of video that preceding step is obtained carries out front and rear extension multi-frame processing, and then the corresponding frame number of these key frame of video after expanding frame processing is stored entirely in same crucial frame number queue；Editing.
Description
Technical field
Field of intelligent video surveillance in terms of the invention belongs to security protection, specifically belong to a kind of and be based on existing monitor supervision platform
Video early stage Intelligent treatment technology.
Background technology
In traditional safety-security area, relevant staff need to carry out video more than times check repeatedly could be at some
Some favourable clues are found in details, it is such so as to which further video is analyzed and handled according to these clues
Requirement of the process to relevant staff is very high, it is necessary to the staring concern screen of staff, spiritual need high concentration,
So that the working strength of related personnel is excessive；On the other hand, the notice focus of people has cycle regular hour, reaches
After regular hour length, the situation of distraction just occurs in people, be thus likely to result in video contain compared with
The key frame of high price value information it is ignored fall, be unable to reach initial purpose and demand.
Based on such present situation, if can be carried out just to some more tediously long monitor videos by some effective algorithms
The editing processing of step, shortens video length, then gives that relevant staff is further to be handled, and so can both mitigate significantly
The burden of staff, it can increase work efficiency again.
With the continuous development of the technologies such as image steganalysis, computer vision, various intelligent algorithms are constantly suggested, closely
In the past few years, machine learning, deep learning are also concerned by people further, and attract increasing people to bound oneself to it, correlative study
Deepen continuously.Personage in video is analyzed, on condition that carrying out Preliminary detection to the personage in video, and presently, there are
Some personnel's detection algorithms provide strong theories integration to solve actual engineering problem.The conventional side of human testing at present
Method is extraction human body key feature and the corresponding grader of training, and the classical conventional method of early stage is the gradient of Dalal et al. designs
The combination of histogram feature and SVM classifier, this method promotes the breakthrough in pedestrian detection field, similar, right
That answers also has the features such as LBP, Harr, and the grader designed the methods of Adaboost graders, neutral net.In addition, work as
It is pedestrian detection that this field of the preceding detection to people is most of, and the substantially pedestrian in parallel views, human body contour outline
Than more complete, but because the position of monocular cam in practice is fixed, enter the monitoring field of camera from pedestrian and take the photograph to walking out
As the monitoring field of head, the profile of pedestrian is not necessarily always complete, and sometimes only part human body is appeared in monitor video,
Moreover, with the change of distance, the human body image in video is not of uniform size, at this time only fears using only certain single grader
Fearness can not meet the needs of actual.For such case, if the people for being adapted to a variety of environment in monitor video can be designed
Grader is surveyed in physical examination, retains more key frames by reaching higher personnel targets verification and measurement ratio, it is possible to is preferably ensured
Video quality after editing processing.
Meanwhile concern of the China for intelligent security guard field is also increasingly deep, video monitoring system spreads over production and living
In, and the monitor video to obtaining be further analyzed processing just become a vital task.By using one kind compared with
Early stage editing processing is carried out to video for effective personnel's detection algorithm, can greatly mitigate the work load of staff,
And the analytical cycle of video is greatly shortened, the work efficiency of relevant departments is improved, this has one for intelligent security guard field
Determine meaning.
The content of the invention
It is an object of the invention to overcome the shortcomings of the high human cost of analyzing and processing monitor video in existing safety-security area,
One kind is provided and is based on existing monitor supervision platform, can be as follows to the method for the intelligent editing processing of video progress, technical scheme：
A kind of video intelligent clipping method based on monitor supervision platform, including following step：
1) input video, video is read frame by frame, all frame of video are normalized into unified size, then frame of video carried out
Pretreatment, so that subsequent step is further handled；
2) to pretreated frame of video, sport foreground, and the sport foreground point number in statistics frame are extracted；
3) threshold process is carried out to foreground point number, retains the frame of video that foreground point number is more than threshold value, exclude foreground point
The very few frame of video of number；
4) to the video frame extraction for meeting threshold condition its sport foreground profile remained, sport foreground profile is entered
The appropriate Morphological scale-space of row, then draws its bounding rectangles frame, according to detection demand in practice, takes and suitably amplify its side
The strategy of boundary's rectangle frame, the width of rectangle frame amplify 1.1 times, highly enlarged 1.2 times of rectangle frame；
5) obtained sport foreground bounding rectangles frame is further processed, calculates the face of sport foreground bounding rectangles frame
Product and the ratio of width to height, if sport foreground bounding rectangles frame area is too small or aspect ratio does not meet the general features of human body,
The sport foreground is excluded, and then excludes the frame of video；
6) discriminatory analysis in summary, a part of doubtful frame of video containing personnel is tentatively remained, counts its quantity,
If the frame number after statistics is very few, video clipping processing below is no longer carried out, saves as former video；Conversely, then continue more
Each video frame images of dimensional scan input, are detected and are sentenced to it with the human testing assembled classifier of pre-training
It is disconnected, it will be deemed as the frame of video comprising human body and be defined as key frame of video；
Herein, the human testing assembled classifier of pre-training is as follows：
Multiple dimensioned scanning is first carried out to each video frame images of input, then, by using being suitable in monitor video
The human testing assembled classifier of a variety of environment, human testing assembled classifier include upper half of human body detection cascade classifier
Cascade classifier is detected with the human body lower part of the body, and upper half of human body detection cascade classifier contains the head-and-shoulder area of human body and right
The body frame structural information answered, it is ensured that higher human testing rate, when only existing part human body in video image, especially
It is when human body head information is not present, can be then compensated its defect using lower part of the body detection cascade classifier；
7) information correlativity between frame of video is considered, the key frame of video that preceding step is obtained carries out front and rear extend
Multi-frame processing, these corresponding frame numbers of key frame of video after expanding frame processing are then stored entirely in same key frame
In number queue；
8) read former video frame by frame again, judge that the frame number of each frame of video whether there is in the crucial frame number being previously obtained
In queue, if in the presence of, correspond to retain the key frame of video, most at last these key frame of video remained reconfigure for
One new video preserves.
Color information of the present invention independent of video frame images, the monitor video at night can also be handled, by more
Completely key frame of video of the overwhelming majority comprising personnel is preserved, it is rational to remove the video without larger break-up value
Frame, video length is shortened, effectively realizes the early stage editing processing of video, significantly reduce the work load of staff,
Human cost is reduced, improves efficiency.
Brief description of the drawings
The video monitoring system block diagram that Fig. 1 is carried by the inventive method
Fig. 2 is the detecting system of human body block diagram designed by the inventive method
Fig. 3 is the partial test result figure of the detecting system of human body designed by the inventive method
Fig. 4 is the flow chart of the inventive method
Embodiment
The general processing framework that video monitoring system is treated in existing safety-security area is：The picture shot by analog video camera
Face is directly transmitted to monitor by a cable part and shown, another part is transmitted to DVR.Into the mould of DVR
Intend signal and be changed into digital code stream, on the one hand encoded, be stored in document form in DVR；On the other hand, may be used
DVR is connected by network at any time, extraction code stream is shown, analyzed.It is specific as shown in Figure 1.Itd is proposed based on the present invention
Method formed video early stage processing software, be by extraction be stored in DVR the video after transcoding text
Part, then the video is pre-processed, input detecting system of human body, by multiple dimensioned scan video two field picture frame by frame and make
Detected with the human testing assembled classifier of pre-training, will be determined as that the frame of video comprising personnel is remained as key
Frame, and to its appropriate expansion frame, the key frame after expanding frame processing is reassembled into new video, by removing without larger analysis
The frame of video of value, video length is shortened, effectively realize the early stage intelligence editing processing of video.
The inventive method is specifically as shown in Figure 4.
Various pieces are described in detail below：
1. frame of video pre-processes
The problem of in view of computational efficiency, the frame of video of reading is normalized, it is big that CIF forms are uniformly processed into
It is small, because monitor video is much night video, so frame of video is converted into gray scale bitmap-format by rgb signal, so
With independent of color information, and carry out appropriate histogram equalization processing and avoid disturbing caused by frame of video jump in brightness.
2. sport foreground is extracted
What is taken in this programme is the foreground extracting method of mixed Gauss model, by carrying out background modeling and the in good time back of the body
Scape updates, and carries out appropriate morphologic filtering processing to the sport foreground of extraction, includes the processing such as dilation erosion, can obtain
To more complete sport foreground.
3. foreground point counts and processing
The foreground point number in the sport foreground extracted is counted, and appropriate threshold value constraint is carried out to it, gives up and includes
The very few frame of video of foreground point number.
4. sport foreground contours extract
To qualified video frame extraction its sport foreground profile remained, it is carried out at appropriate morphology
Reason, then draws its bounding rectangles frame, according to detection demand in practice, takes the strategy for suitably amplifying its bounding rectangles frame,
The width of rectangle frame amplifies about 1.1 times, highly enlarged about 1.2 times of rectangle frame, so can walk follow-up detection of classifier
Rapid verification and measurement ratio is higher, improves precision；
5. frame of video is chosen in advance
Obtained sport foreground bounding rectangles frame is further processed, calculates its area and size, if fortune
Dynamic prospect bounding rectangles frame area is too small or aspect ratio does not meet the general features of human body, then excludes the sport foreground, enter
And exclude the frame of video；
6. detection of classifier
In summary discriminatory analysis, a part of doubtful frame of video containing personnel is tentatively remained, counts its quantity, such as
Frame number after fruit statistics is very few, then no longer carries out video clipping processing below, save as former video；Conversely, then continue it
It is input in detecting system of human body below, each video frame images of multiple dimensioned scanning input, with the people trained
Physical examination surveys grader and it is detected and judged, will be deemed as the frame of video comprising human body and is defined as key frame of video.
Herein, the good human testing classifier design process of pre-training is as shown in Figure 2.
Various pieces are described in detail below：
In view of the profile information of human body in actual monitored video, the not necessarily complete and profile of human body is not of uniform size, herein
Take following scheme：Multiple dimensioned scanning is first carried out to each video frame images of input, then, by using fitting for design
The human testing assembled classifier of a variety of environment in monitor video is answered, reaches higher human testing rate, by most video
Key frame more completely preserves.
For the consideration to the Video processing time, what is taken herein is the strategy of cascade classifier, specifically, people's physical examination
Survey assembled classifier and include upper half of human body detection cascade classifier and human body lower part of the body detection cascade classifier.Human body upper half
Body detection cascade classifier contains the head-and-shoulder area of human body and corresponding body frame structural information, it is ensured that higher
Human testing rate, but, when only existing part human body in video image, especially in the absence of human body head information when, under
Half body detection cascade classifier can then compensate its defect.
Specifically, there is employed herein histogram of gradients feature extraction and cascade Adaboost algorithm design assembled classifier
Scheme.It is to cascade multiple strong classifiers to cascade Adaboost algorithm, and each strong classifier therein is by multiple weak
What classifiers combination formed, the two kinds of cascade classifiers designed herein are set as 18 grades.By training substantial amounts of positive negative sample
Preliminary the upper half of human body detection cascade classifier for having obtained better performances and lower part of the body detection cascade classifier, then by this two
Acted on after kind classifiers combination on test set, analyze test result.According to test result, increase corresponding to this paper and perfect
Positive negative sample, including the upper half of human body positive sample at a variety of visual angles and the negative sample for easily causing interference, such as trees, building, electricity
Line bar, roadblock etc., the diversity of sample is improved, effectively improves classifier performance, improves verification and measurement ratio, reduce wrong report
Rate.
Generally speaking, by multiple dimensioned scan video two field picture and using human testing assembled classifier, original can be regarded
Key frame of the overwhelming majority comprising personnel more completely preserves in frequency, the video matter being effectively guaranteed after editing processing
Amount.
7. key frame stores
In view of the information correlativity between frame of video, the key frame of video that preceding step is obtained carries out front and rear extension 15
Frame processing, these corresponding frame numbers of key frame of video after expanding frame processing are then stored entirely in same crucial frame number
In queue；
8. it is reassembled as new video
Again former video is read frame by frame, judges that the frame number of each frame of video whether there is in the crucial frame number team being previously obtained
In row, if in the presence of corresponding to retain the key frame of video, most these key frame of video remained are reconfigured as one at last
Individual new video preserves.By more completely remaining the key frame of video containing key message, eliminate without larger point
The frame of video of value is analysed, shortens video length, effectively realizes the early stage intelligence editing processing of video.
Claims (1)
1. a kind of video intelligent clipping method based on monitor supervision platform, including following step：
1) input video, video is read frame by frame, all frame of video are normalized into unified size, then frame of video located in advance
Reason, so that subsequent step is further handled；
2) to pretreated frame of video, sport foreground, and the sport foreground point number in statistics frame are extracted；
3) threshold process is carried out to foreground point number, retains the frame of video that foreground point number is more than threshold value, exclude foreground point number
Very few frame of video；
4) to the video frame extraction for meeting threshold condition its sport foreground profile remained, sport foreground profile is fitted
When Morphological scale-space, then draw its bounding rectangles frame, according to detection demand in practice, take and suitably amplify its shape based moment
The strategy of shape frame, the width of rectangle frame amplify 1.1 times, highly enlarged 1.2 times of rectangle frame；
5) obtained sport foreground bounding rectangles frame is further processed, calculate sport foreground bounding rectangles frame area and
The ratio of width to height, if sport foreground bounding rectangles frame area is too small or aspect ratio does not meet the general features of human body, exclude
The sport foreground, and then exclude the frame of video；
6) discriminatory analysis in summary, a part of doubtful frame of video containing personnel is tentatively remained, counts its quantity, if
Frame number after statistics is very few, then no longer carries out video clipping processing below, save as former video；Conversely, then continue multiple dimensioned
Each video frame images of input are scanned, it is detected and judged with the human testing assembled classifier of pre-training, will
It is judged as that the frame of video comprising human body is defined as key frame of video；
Herein, the human testing assembled classifier of pre-training is as follows：
Multiple dimensioned scanning is first carried out to each video frame images of input, it is then, a variety of in monitor video by using being suitable for
The human testing assembled classifier of environment, human testing assembled classifier include upper half of human body detection cascade classifier and people
The body lower part of the body detects cascade classifier, and upper half of human body detection cascade classifier contains the head-and-shoulder area of human body and corresponding
Body frame structural information, it is ensured that higher human testing rate, when only existing part human body in video image, especially not
When human body head information being present, it can then be compensated its defect using lower part of the body detection cascade classifier；
7) information correlativity between frame of video is considered, the key frame of video that preceding step is obtained carries out front and rear extension multiframe
Processing, these corresponding frame numbers of key frame of video after expanding frame processing are then stored entirely in same crucial frame number team
In row；
8) read former video frame by frame again, judge that the frame number of each frame of video whether there is in the crucial frame number queue being previously obtained
In, if in the presence of corresponding to retain the key frame of video, most these key frame of video remained are reconfigured as one at last
New video preserves.
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| CN109583441A (en) * | 2018-10-19 | 2019-04-05 | 天津大学 | Subtitle recognition method based on monitor supervision platform |
| CN111611944A (en) * | 2020-05-22 | 2020-09-01 | 创新奇智(北京)科技有限公司 | Identity recognition method and device, electronic equipment and storage medium |
| CN111738196A (en) * | 2020-06-30 | 2020-10-02 | 上海松鼠课堂人工智能科技有限公司 | Learning state tracking and analysis device and system |
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Application publication date: 20180216 |