CN102833492A - Color similarity-based video scene segmenting method - Google Patents
Color similarity-based video scene segmenting method Download PDFInfo
- Publication number
- CN102833492A CN102833492A CN2012102736947A CN201210273694A CN102833492A CN 102833492 A CN102833492 A CN 102833492A CN 2012102736947 A CN2012102736947 A CN 2012102736947A CN 201210273694 A CN201210273694 A CN 201210273694A CN 102833492 A CN102833492 A CN 102833492A
- Authority
- CN
- China
- Prior art keywords
- scene
- value
- cut
- point
- video segment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Landscapes
- Television Signal Processing For Recording (AREA)
- Compression Or Coding Systems Of Tv Signals (AREA)
Abstract
The invention belongs to the technical field of computer video data processing, and relates to a color similarity-based video scene segmenting method. According to the color similarity-based video scene segmenting method, for a video clip, a similarity matrix is obtained according to the following method: similarity among frames is computed by using RGB (Red, Green, Blue) color histograms of all frames, and a similarity matrix of the whole clip range is obtained. The color similarity-based video scene segmenting method comprises the following steps of: carrying out first-time scene segmentation on the video clip; merging small scenes; and verifying a transition section and then segmenting. According to the color similarity-based video scene segmenting method, the scene is segmented, and a more accurate and reliable segmenting result can be obtained.
Description
Affiliated technical field
The invention belongs to the computer video technical field of data processing, the method that particularly a kind of video scene is cut apart.
Background technology
Along with the fast development of digital multimedia technology and Computer Storage ability, digital video has obtained extensive use in people's work and life in recent years.Yet how a large amount of digital of digital video data effectively being retrieved and managed is the difficult problem that need solve present stage.Video scene is cut apart as critical basic steps, on addressing this problem, seems particularly important.Obviously, be divided into some set of segments with remarkable stand-alone content to a complete video abstract extraction and the content retrieval of complete video had important function.
Summary of the invention
The object of the present invention is to provide a kind of simple and effective video scene dividing method.The methods of video segmentation that the present invention proposes has only been used the color characteristic of each two field picture in the video, and for the extraction of cataclysm cut-point between video segment and transition fragment reasonable effect has been arranged all.For this reason, the present invention adopts following technical scheme:
A kind of video scene dividing method based on the color similarity degree; This method is for a video segment; Obtain similarity matrix according to following method: the RGB color histogram of all frames, utilize the similarity between the histogram calculation frame, obtain the similarity matrix of entire segment scope; And, stipulated a classification that scene transition arrives the scene version of another scene:
(1) cataclysm cut-point: directly turn to another scene from a scene, the centre does not have transition;
The changeover portion of (2) being fade-in fade-out: turn to the process of another scene from a scene, when previous scene was faded out, a back scene was faded in;
(3) changeover portion of complicacy: other scene versions that can not be divided into above-mentioned two kinds of situation;
This method comprises following step:
The scene first that step 1 is carried out video segment according to following method is cut apart:
(1) include first frame in first scene, with second frame as present frame;
(2) calculate the mean value avg0 of similarity matrix all values of the previous scene of present frame, and the mean value avg1 of the similarity vector of it and previous all frames of scene;
(3) if | < consistency threshold value TH1 then includes present frame in previous scene to avg0-avg1|, and the following frame of present frame as present frame, is continued (2); Otherwise, be starting point with the present frame, begin new scene, the following frame of present frame as present frame, and is continued (2), stop up to video segment, thereby obtain segmentation result;
Step 2 merges little scene: in segmentation result, merge several continuous frame numbers less than the scene that merges threshold value TH2;
Step 3 checking changeover portion is also cut apart again: calculate the standard deviation that merges similarity matrix all values in the scene domain that forms; If standard deviation is less than changeover portion discrimination threshold TH3; Then the scene after this merging is judged as pseudo-changeover portion, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging so far obtains new segmentation result.
As preferred implementation, the training of three threshold values is wherein carried out according to the following step:
(1) obtain a collection of video segment that is used to train, each real scene cut-point of this batch video segment carried out the classification of scene version from a scene transition to another scene:
(2) set initial value and stop value and the cumulative unit step-length of consistency threshold value TH1, and set initial merging threshold value TH2 and changeover portion discrimination threshold TH3;
(3) to each video segment that is used to train, carry out first scene according to the method in the step 1 and cut apart, obtain segmentation result;
(4) each real scene cut-point of segmentation result and video segment is compared: with segmentation result comparison real scene cut-point calculation cost value a and coverage rate b; And estimate the degree of agreement cut apart with the real scene cut-point with
; Wherein, the calculating of cost value a is following:
A is initially 0; Each of segmentation result is cut apart the frame at place, in the real scene cut-point, seek it, if can in the real scene cut-point, find, and this locates to be the cataclysm cut-point, then a is not done any operation; If can in the real scene cut-point, find, and be in the changeover portion or complicated changeover portion of being fade-in fade-out, then
The calculating of coverage rate b is following:
Introduce variable a1, a1 is initially 0; Each cuts apart the frame at place to the real scene cut-point, in segmentation result, seeks it, if can in segmentation result, find, then a1 is not done any operation, if can't find, and a1=a1+1 then; Calculate at last
(5) value that constantly changes TH1 makes
minimum, the TH1 trained values of the video segment that obtains being used to train;
(6) mean value of TH1 trained values of video segment of getting the training that is useful on is as the recommended value of consistency threshold value;
The recommended value of the consistency threshold value that (7) obtains with above-mentioned training is TH1, carries out first scene according to the method for step 1 again and cuts apart;
(8) set initial value and stop value and the cumulative unit step-length that merges threshold value TH2;
(9) all merge into a scene to several continuous frame numbers less than the scene of TH2;
(10) will merge result that little scene obtains according to the method for step (4) and the real scene cut-point of video segment is compared;
(11) value that constantly changes TH2 makes
minimum, the TH2 trained values of the video segment that obtains being used to train;
(12) mean value of TH2 trained values of video segment of getting the training that is useful on is as the recommended value that merges threshold value;
The recommended value of the consistency threshold value that (13) obtains with above-mentioned training is TH1, and the recommended value that merges threshold value is TH2, carries out first scene according to the method for step 1 and cuts apart, and obtains segmentation result;
(14) calculate the standard deviation that merges similarity matrix all values in the scene domain that forms, less than TH3, then the scene after this merging is judged as pseudo-changeover portion as if standard deviation, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging;
(15) segmentation result that obtains after with above judgement according to the method for step (4) and the real scene cut-point of video segment are compared;
(16) value that constantly changes TH3 makes
minimum, the TH3 trained values of the video segment that obtains being used to train;
(17) mean value of TH3 trained values of video segment of getting the training that is useful on is as the recommended value of changeover portion discrimination threshold.
Scene dividing method of the present invention is to cataclysm cut-point, the section of cutting apart of being fade-in fade-out, and the complicated dividing section can both obtain effect preferably.Setting threshold is the value of a certain scope and carries out scene according to the present invention and cut apart, and then can access segmentation result (Fig. 1) comparatively accurately, explains that methods of video segmentation of the present invention has certain effect.In order to make segmentation effect better, be necessary threshold value is trained, training effect can be through verifying with the comparison of real video scene cut-point.In confirmatory experiment, we have used 50 video segments, and each video segment comprises several known real scene cut-points.In experiment, we use following two statisticss to weigh the reliability of overall training result.
(1) cost value a:, according to said method cut apart all cut-points that obtain and remove to compare the real scene cut-point, the relative cost value of the quantity of unnecessary experimental result cut-point to a video segment.
(2) coverage rate b: to a video segment, according to said method cut apart in all cut-points that obtain, the cut-point quantity that conforms to the real scene cut-point accounts for the ratio of real scene cut-point sum.
Can know that from above two indexs cost value a is more little to represent that then unnecessary experimental result cut-point is few more, the validity score cutpoint that coverage rate b approaches 1 expression omission more is few more.Through checking to 50 video segments; Cost value a is all less among the result; And coverage rate b remains on (Fig. 2) more than 80% substantially, and then the threshold value recommended value that obtains of training is reliably, and segmentation effect of the present invention and real video scene cut-point degree of agreement are higher.
Description of drawings
Fig. 1 segmentation result figure, last figure, middle figure and figure below part are the segmentation result sketch map of cataclysm cut-point, the section of cutting apart of being fade-in fade-out and complicated dividing section.
Fig. 2 verifies sketch map as a result, and Fig. 2 (a) and Fig. 2 (b) are respectively the statistics of cost value a and the statistics of coverage rate b.
Fig. 3 system flow chart of the present invention.
Embodiment
The present invention proposes a kind of new video scene dividing method.At first the scene conversion form from a scene transition to another scene is made classification, as follows:
(1) cataclysm cut-point: directly turn to another scene from a scene, the centre does not have transition.
Characteristics: scene conversion is very rapid, and frame difference is obvious before and after the cut-point, accurately belongs in the scene separately easily.
The changeover portion of (2) being fade-in fade-out: turn to the process of another scene from a scene, when previous scene was faded out, a back scene was faded in.
Characteristics: scene conversion is slow relatively, and changeover portion often comprises the image of several frames to tens number of frames, and difference is greater than the common scenarios section between each frame.
(3) complicated changeover portion: from a scene turns to the process of another scene, follow certain complicated mapping mode, like dissolving, amplification etc.(other scene versions that can not be divided into above-mentioned two kinds of situation all divide " complicated changeover portion " into)
Characteristics: variation pattern is various, and scene conversion is slow, and changeover portion often comprises the image of several frames to tens number of frames, and difference is greater than the common scenarios section between each frame.
Scene dividing method of the present invention at first utilizes " consistency threshold value " according to algorithm video to be carried out cutting apart the first time; Again each scene after cutting apart is reconsolidated changeover portion according to " merging threshold value "; Filter pseudo-changeover portion through " changeover portion discrimination threshold " at last, thereby get scene segmentation result to the end.Specifying as follows of these three kinds of threshold values:
(1) consistency threshold value: when cutting apart first, determine whether next frame is included in the previous scene according to algorithm.
(2) merge threshold value: after cutting apart first, several continuous frame numbers all will be merged into a scene (or changeover portion) less than the scene of this threshold value.
(3) changeover portion discrimination threshold: after little scene merges, calculate the standard deviation that merges similarity matrix all values in the scene domain that forms.If standard deviation less than this threshold value, then is judged as pseudo-changeover portion, should be reduced to the state before merging; Otherwise this scene is a changeover portion.
Referring to Fig. 3, optimum implementation of the present invention is following:
Step 1. is treated the RGB color histogram that divided video (N frame) extracts all two field pictures, R:32, G:32, B:32 totally 96 dimensional features wherein, and promptly the histogram matrix size is 96*N; Utilize all frames of histogram calculation similarity between any two, thereby obtain the similarity matrix (N*N) of entire segment scope.
Step 2. scene is first cut apart: according to following algorithm divided video fragment.
Algorithm:
(1) includes first frame in first scene.
(2) to second frame and frame afterwards, calculate the mean value avg0 of the similarity matrix all values of its previous scene, and the mean value avg1 of the similarity vector of it and previous all frames of scene.
(3) if | < TH1 (consistency threshold value) then includes present frame in previous scene to avg0-avg1|, continues (2);
Otherwise, be starting point with the present frame, begin new scene, and continuation (2).Stop up to video segment, thereby obtain segmentation result.
Be the specific explanations that relevant similarity matrix calculates below: as far as a scene, establishing p is start frame, and q is an end frame, and present frame is k (k be and then first frame of q)
Avg0: all frames are done similarity relatively in twos from p to q, and the matrix of (p-q+1) * (p-q+1) size that obtains is similarity matrix, and the mean value of all values is avg0 in the matrix
Avg1:k and p do similarity relatively to all frames of q, the one-dimensional vector of (p-q+1) size that obtains, and the mean value of this vector all values is avg1
Step 3. merges little scene: in segmentation result, merge the scene of several continuous frame numbers less than TH2 (merging threshold value), as the changeover portion of hypothesis.
Step 4. checking changeover portion is also cut apart again: analyze and obtain the frame difference of the interior frame difference of changeover portion greater than common scenarios, therefore calculate the standard deviation that merges similarity matrix all values in the scene domain that forms.If standard deviation is less than TH3 (changeover portion discrimination threshold), then the scene after this merging is judged as pseudo-changeover portion, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging.So far obtain new segmentation result.
Step 5. is in order to obtain better segmentation result; We are necessary consistency threshold value TH1, merging threshold value TH2 and these three threshold values of changeover portion discrimination threshold TH3 are proposed more reasonable suggestions value; Therefore adopt the method for training, these three threshold values are trained through multitude of video.Concrete training method is following:
(1) training preliminary step
Obtain the RGB color histogram of all frames of video segment (50 fragments, each 2000-6000 frame does not wait), wherein R:32, G:32, B:32 totally 96 dimensional features.Utilize histogram that each frame is calculated similarity between any two, obtain the similarity matrix of entire segment scope.
(2) consistency threshold value training
1. be consistency threshold value TH1 setting initial value and stop value (artificially controlling this scope), and cumulative unit step-length.
2. with TH1 threshold value, the algorithm divided video fragment in 2 set by step.
3. the result who divided video is obtained and the real scene cut-point of video segment are compared.With experimental result comparison real scene cut-point calculation cost value a (the more little then experimental result of a value major part can both be found in the real scene cut-point); Omit the degree b (more little then the explanation in the real scene cut-point of b value much can't be found, i.e. experimental result omission real scene cut-point degree is big) of real scene cut-point in addition in experimental result with real scene cut-point control experiment experiment with computing result as a result.Finally with the degree of agreement of
evaluation experimental result and real scene cut-point, R value more greatly then experimental result and real scene cut-point is identical more.
A. the calculating of cost value a:
A is initially 0.
Each cuts apart the frame at place to experimental result, in the real scene cut-point, seeks it, if can in the real scene cut-point, find, and this locates to be the cataclysm cut-point, then a is not done any operation.
If can in the real scene cut-point, find; And be in the changeover portion scope (changeover portion of being fade-in fade-out or complicated changeover portion) then
If can't find, a=a+1 then
B. the calculating of coverage rate b:
Introduce the variable a1 of similar a in the calculating of b, a1 is initially 0.
Each cuts apart the frame at place to the real scene cut-point, in experimental result, seeks it, if can in experimental result, find (being cataclysm cut-point or the changeover portion of being fade-in fade-out or complicated changeover portion) in the real scene cut-point, then a1 is not done any operation.
If can't find, a1=a1+1 then.
4. constantly change feasible
minimum of value of TH1; Promptly under the TH1 threshold value, segmentation result and real scene cut-point are the most identical.
5. 50 videos are all trained TH1 one time, the average of getting them is as consistency threshold value recommended value.
(3) merge the threshold value training
1. the best TH1 (being obtained by the training of (2) consistency threshold value) with experiment fragment self is a threshold value, presses and step 2 identical algorithms divided video fragment.
2. set initial value and stop value (artificially controlling this scope) for merging threshold value TH2, cumulative unit step-length is 1.
3. all merge into a scene to several continuous frame numbers less than the scene of TH2.
4. will merge the result that little scene obtains and the real scene cut-point of video segment compares.With experimental result comparison real scene cut-point calculation cost value a, omit the degree b of real scene cut-point in addition with real scene cut-point control experiment experiment with computing result as a result.Finally with the degree of agreement of
evaluation experimental result and real scene cut-point.The computational methods of cost value a and coverage rate b are constant.
5. constantly change feasible
minimum of value of TH2; Promptly under the TH2 threshold value, segmentation result and real scene cut-point are the most identical.
6. 50 videos are all trained TH2 one time, the average of getting them is as merging the threshold value recommended value.
(4) changeover portion discrimination threshold training
1. the best TH1 (being obtained by the training of A. consistency threshold value) with experiment fragment self is a threshold value, presses and step 2 identical algorithms divided video fragment.
2. the best TH2 (merging the threshold value training by B. obtains) with experiment fragment self is a threshold value, all merges into a scene to several continuous frame numbers less than the scene of TH2.
3. be changeover portion discrimination threshold TH3 setting initial value and stop value (artificially controlling this scope), and cumulative unit step-length.
4. calculate the standard deviation that merges similarity matrix all values in the scene domain that forms.If standard deviation is less than TH3, then the scene after this merging is judged as pseudo-changeover portion, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging.
5. the real scene cut-point of result who obtains after the above judgement and video segment is compared.With experimental result comparison real scene cut-point calculation cost value a, omit the degree b of real scene cut-point in addition with real scene cut-point control experiment experiment with computing result as a result.Finally with the degree of agreement of
evaluation experimental result and real scene cut-point.The computational methods of cost value a and coverage rate b are constant.
6. constantly change feasible
minimum of value of TH3; Promptly under the TH3 threshold value, segmentation result and real scene cut-point are the most identical.
7. 50 videos are all trained TH3 one time, the average of getting them is as changeover portion discrimination threshold recommended value.
Step 6. is used the sample of 50 video segments (2000-6000 frame) in order to verify the reliability of training result, and three recommended values that obtain with training are as threshold value TH1, TH2, and TH3 carries out scene to 50 videos respectively and cuts apart.Assay demonstration recommended value is cut apart effectively video scene.
Step 7. is used for later video with three recommended values of training result as threshold value and cuts apart, and carries out video scene according to the method for step 1 to step 4 and cuts apart and can obtain better segmentation effect.
Claims (2)
1. video scene dividing method based on the color similarity degree; This method is for a video segment; Obtain similarity matrix according to following method: the RGB color histogram of all frames, utilize the similarity between the histogram calculation frame, obtain the similarity matrix of entire segment scope; And, stipulated a classification that scene transition arrives the scene version of another scene:
(1) cataclysm cut-point: directly turn to another scene from a scene, the centre does not have transition;
The changeover portion of (2) being fade-in fade-out: turn to the process of another scene from a scene, when previous scene was faded out, a back scene was faded in;
(3) changeover portion of complicacy: other scene versions that can not be divided into above-mentioned two kinds of situation;
This method comprises following step:
The scene first that step 1 is carried out video segment according to following method is cut apart:
(1) include first frame in first scene, with second frame as present frame;
(2) calculate the mean value avg0 of similarity matrix all values of the previous scene of present frame, and the mean value avg1 of the similarity vector of it and previous all frames of scene;
(3) if | < consistency threshold value TH1 then includes present frame in previous scene to avg0-avg1|, and the following frame of present frame as present frame, is continued (2); Otherwise, be starting point with the present frame, begin new scene, the following frame of present frame as present frame, and is continued (2), stop up to video segment, thereby obtain segmentation result;
Step 2 merges little scene: in segmentation result, merge several continuous frame numbers less than the scene that merges threshold value TH2;
Step 3 checking changeover portion is also cut apart again: calculate the standard deviation that merges similarity matrix all values in the scene domain that forms; If standard deviation is less than changeover portion discrimination threshold TH3; Then the scene after this merging is judged as pseudo-changeover portion, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging so far obtains new segmentation result.
2. the video scene dividing method based on the color similarity degree according to claim 1 is characterized in that, the training of three threshold values is wherein carried out according to the following step:
(1) obtain a collection of video segment that is used to train, each real scene cut-point of this batch video segment carried out the classification of scene version from a scene transition to another scene:
(2) set initial value and stop value and the cumulative unit step-length of consistency threshold value TH1, and set initial merging threshold value TH2 and changeover portion discrimination threshold TH3;
(3) to each video segment that is used to train, carry out first scene according to the method in the step 1 and cut apart, obtain segmentation result;
(4) each real scene cut-point of segmentation result and video segment is compared: with segmentation result comparison real scene cut-point calculation cost value a and coverage rate b; And estimate the degree of agreement cut apart with the real scene cut-point with
; Wherein, the calculating of cost value a is following:
A is initially 0; Each of segmentation result is cut apart the frame at place, in the real scene cut-point, seek it, if can in the real scene cut-point, find, and this locates to be the cataclysm cut-point, then a is not done any operation; If can in the real scene cut-point, find, and be in the changeover portion or complicated changeover portion of being fade-in fade-out, then
The calculating of coverage rate b is following:
Introduce variable a1, a1 is initially 0; Each cuts apart the frame at place to the real scene cut-point, in segmentation result, seeks it, if can in segmentation result, find, then a1 is not done any operation, if can't find, and a1=a1+1 then; Calculate at last
(5) value that constantly changes TH1 makes
minimum, the TH1 trained values of the video segment that obtains being used to train;
(6) mean value of TH1 trained values of video segment of getting the training that is useful on is as the recommended value of consistency threshold value;
The recommended value of the consistency threshold value that (7) obtains with above-mentioned training is TH1, carries out first scene according to the method for step 1 again and cuts apart;
(8) set initial value and stop value and the cumulative unit step-length that merges threshold value TH2;
(9) all merge into a scene to several continuous frame numbers less than the scene of TH2;
(10) will merge result that little scene obtains according to the method for step (4) and the real scene cut-point of video segment is compared;
(11) value that constantly changes TH2 makes
minimum, the TH2 trained values of the video segment that obtains being used to train;
(12) mean value of TH2 trained values of video segment of getting the training that is useful on is as the recommended value that merges threshold value;
The recommended value of the consistency threshold value that (13) obtains with above-mentioned training is TH1, and the recommended value that merges threshold value is TH2, carries out first scene according to the method for step 1 and cuts apart, and obtains segmentation result;
(14) calculate the standard deviation that merges similarity matrix all values in the scene domain that forms, less than TH3, then the scene after this merging is judged as pseudo-changeover portion as if standard deviation, is reduced to the little scene state before merging; Otherwise, judge that this scene is a changeover portion, the state after keeping merging;
(15) segmentation result that obtains after with above judgement according to the method for step (4) and the real scene cut-point of video segment are compared;
(16) value that constantly changes TH3 makes
minimum, the TH3 trained values of the video segment that obtains being used to train;
The mean value of TH3 trained values of video segment of getting the training that is useful on is as the recommended value of changeover portion discrimination threshold.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210273694.7A CN102833492B (en) | 2012-08-01 | 2012-08-01 | A kind of video scene dividing method based on color similarity |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201210273694.7A CN102833492B (en) | 2012-08-01 | 2012-08-01 | A kind of video scene dividing method based on color similarity |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN102833492A true CN102833492A (en) | 2012-12-19 |
| CN102833492B CN102833492B (en) | 2016-12-21 |
Family
ID=47336437
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201210273694.7A Active CN102833492B (en) | 2012-08-01 | 2012-08-01 | A kind of video scene dividing method based on color similarity |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN102833492B (en) |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103533255A (en) * | 2013-10-28 | 2014-01-22 | 东南大学 | Motion displacement curve simplification based automatic segmentation method for video scenes |
| CN104394422A (en) * | 2014-11-12 | 2015-03-04 | 华为软件技术有限公司 | Video segmentation point acquisition method and device |
| CN108509917A (en) * | 2018-03-30 | 2018-09-07 | 北京影谱科技股份有限公司 | Video scene dividing method and device based on shot cluster correlation analysis |
| CN108647641A (en) * | 2018-05-10 | 2018-10-12 | 北京影谱科技股份有限公司 | Video behavior dividing method and device based on two-way Model Fusion |
| WO2019080685A1 (en) * | 2017-10-24 | 2019-05-02 | 北京京东尚科信息技术有限公司 | Video image segmentation method and apparatus, storage medium and electronic device |
| CN109740499A (en) * | 2018-12-28 | 2019-05-10 | 北京旷视科技有限公司 | Methods of video segmentation, video actions recognition methods, device, equipment and medium |
| CN110248182A (en) * | 2019-05-31 | 2019-09-17 | 成都东方盛行电子有限责任公司 | A kind of scene segment lens detection method |
| CN112511719A (en) * | 2020-11-10 | 2021-03-16 | 陕西师范大学 | Method for judging screen content video motion type |
| CN113160273A (en) * | 2021-03-25 | 2021-07-23 | 常州工学院 | Intelligent monitoring video segmentation method based on multi-target tracking |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101132528A (en) * | 2002-04-12 | 2008-02-27 | 三菱电机株式会社 | Metadata reproduction apparatus, metadata delivery apparatus, metadata search apparatus, metadata re-generation condition setting apparatus |
| CN101719144A (en) * | 2009-11-04 | 2010-06-02 | 中国科学院声学研究所 | Method for segmenting and indexing scenes by combining captions and video image information |
| US20100149424A1 (en) * | 2008-12-15 | 2010-06-17 | Electronics And Telecommunications Research Institute | System and method for detecting scene change |
| CN101753853A (en) * | 2009-05-13 | 2010-06-23 | 中国科学院自动化研究所 | Fusion method for video scene segmentation |
| KR20110032610A (en) * | 2009-09-23 | 2011-03-30 | 삼성전자주식회사 | Apparatus and method for scene segmentation |
-
2012
- 2012-08-01 CN CN201210273694.7A patent/CN102833492B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101132528A (en) * | 2002-04-12 | 2008-02-27 | 三菱电机株式会社 | Metadata reproduction apparatus, metadata delivery apparatus, metadata search apparatus, metadata re-generation condition setting apparatus |
| US20100149424A1 (en) * | 2008-12-15 | 2010-06-17 | Electronics And Telecommunications Research Institute | System and method for detecting scene change |
| CN101753853A (en) * | 2009-05-13 | 2010-06-23 | 中国科学院自动化研究所 | Fusion method for video scene segmentation |
| KR20110032610A (en) * | 2009-09-23 | 2011-03-30 | 삼성전자주식회사 | Apparatus and method for scene segmentation |
| CN101719144A (en) * | 2009-11-04 | 2010-06-02 | 中国科学院声学研究所 | Method for segmenting and indexing scenes by combining captions and video image information |
Non-Patent Citations (1)
| Title |
|---|
| 印勇等: "基于主色跟踪和质心运动的视频场景分割", 《计算机应用研究》 * |
Cited By (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103533255A (en) * | 2013-10-28 | 2014-01-22 | 东南大学 | Motion displacement curve simplification based automatic segmentation method for video scenes |
| CN103533255B (en) * | 2013-10-28 | 2016-06-29 | 东南大学 | Based on the video scene automatic division method that moving displacement curve is simplified |
| CN104394422A (en) * | 2014-11-12 | 2015-03-04 | 华为软件技术有限公司 | Video segmentation point acquisition method and device |
| CN104394422B (en) * | 2014-11-12 | 2017-11-17 | 华为软件技术有限公司 | A kind of Video segmentation point acquisition methods and device |
| WO2019080685A1 (en) * | 2017-10-24 | 2019-05-02 | 北京京东尚科信息技术有限公司 | Video image segmentation method and apparatus, storage medium and electronic device |
| US11227393B2 (en) | 2017-10-24 | 2022-01-18 | Beijing Jingdong Shangke Information Technology Co., Ltd. | Video image segmentation method and apparatus, storage medium and electronic device |
| CN108509917A (en) * | 2018-03-30 | 2018-09-07 | 北京影谱科技股份有限公司 | Video scene dividing method and device based on shot cluster correlation analysis |
| CN108509917B (en) * | 2018-03-30 | 2020-03-03 | 北京影谱科技股份有限公司 | Video scene segmentation method and device based on lens class correlation analysis |
| CN108647641A (en) * | 2018-05-10 | 2018-10-12 | 北京影谱科技股份有限公司 | Video behavior dividing method and device based on two-way Model Fusion |
| CN109740499A (en) * | 2018-12-28 | 2019-05-10 | 北京旷视科技有限公司 | Methods of video segmentation, video actions recognition methods, device, equipment and medium |
| CN110248182A (en) * | 2019-05-31 | 2019-09-17 | 成都东方盛行电子有限责任公司 | A kind of scene segment lens detection method |
| CN112511719A (en) * | 2020-11-10 | 2021-03-16 | 陕西师范大学 | Method for judging screen content video motion type |
| CN113160273A (en) * | 2021-03-25 | 2021-07-23 | 常州工学院 | Intelligent monitoring video segmentation method based on multi-target tracking |
Also Published As
| Publication number | Publication date |
|---|---|
| CN102833492B (en) | 2016-12-21 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102833492A (en) | Color similarity-based video scene segmenting method | |
| CN111327945B (en) | Method and apparatus for segmenting video | |
| CN109918969B (en) | Face detection method and device, computer device and computer readable storage medium | |
| WO2019196130A1 (en) | Classifier training method and device for vehicle-mounted thermal imaging pedestrian detection | |
| WO2016066038A1 (en) | Image body extracting method and system | |
| CN106937114B (en) | Method and device for detecting video scene switching | |
| CN107145870B (en) | Recognition system for human face in video | |
| CN106991370B (en) | Pedestrian retrieval method based on color and depth | |
| CN103198705B (en) | Parking place state automatic detection method | |
| CN103065325B (en) | A kind of method for tracking target based on the polymerization of color Distance geometry Iamge Segmentation | |
| CN109145708A (en) | A kind of people flow rate statistical method based on the fusion of RGB and D information | |
| CN104978567A (en) | Vehicle detection method based on scenario classification | |
| CN110267101B (en) | Unmanned aerial vehicle aerial video automatic frame extraction method based on rapid three-dimensional jigsaw | |
| CN103886609B (en) | Vehicle tracking method based on particle filtering and LBP features | |
| CN102567738B (en) | Rapid detection method for pornographic videos based on Gaussian distribution | |
| CN109242776B (en) | Double-lane line detection method based on visual system | |
| CN110599453A (en) | Panel defect detection method and device based on image fusion and equipment terminal | |
| Shan et al. | A small traffic sign detection algorithm based on modified ssd | |
| Lu et al. | Generating fluent tubes in video synopsis | |
| CN102098449A (en) | Method for realizing automatic inside segmentation of TV programs by utilizing mark detection | |
| CN108010044A (en) | A kind of method of video boundaries detection | |
| CN111783910A (en) | Building project management method, electronic equipment and related products | |
| CN113870196A (en) | Image processing method, device, equipment and medium based on anchor point cutting graph | |
| CN109165542A (en) | Based on the pedestrian detection method for simplifying convolutional neural networks | |
| CN109509345A (en) | Vehicle detection apparatus and method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| C14 | Grant of patent or utility model | ||
| GR01 | Patent grant |