CN102222349A - Prospect frame detecting method based on edge model - Google Patents

Prospect frame detecting method based on edge model Download PDF

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CN102222349A
CN102222349A CN 201110185415 CN201110185415A CN102222349A CN 102222349 A CN102222349 A CN 102222349A CN 201110185415 CN201110185415 CN 201110185415 CN 201110185415 A CN201110185415 A CN 201110185415A CN 102222349 A CN102222349 A CN 102222349A
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frame
edge
prospect
sequence
background
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朱伟兴
纪滨
李新城
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Jiangsu University
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Abstract

The invention discloses a prospect frame detecting method based on an edge model, which is used for prospect frame determination in the analysis of a security monitoring video frame sequence. The method comprises the following steps of: extracting edge frames only containing edge images from sequence frames by means of a pseudo-sphere edge detection operator; counting the presence probability of edge points of the edge frames in frame sequence statistic time, marking a background attribute and a foreground attribute based on a determination condition of distinguishing according to the attributes of the edge pixel points within the current edge frame; in a foreground edge image, if the number of the edge points connected together in the current frame is less than or equal to 2, determining that the points are noise points and removing these points; and if the total number of pixels of the left foreground edge images is less than a noise determination threshold of the sequence frame, determining that the frames are background frames, otherwise, foreground frames. The method of the invention reduces the calculation cost, effectively adapts to the cases of background light change, slow object motion or short stagnation and the like, and creates favourable conditions for subsequent object motion analysis.

Description

A kind of prospect frame detection method based on edge model
Technical field
The present invention relates to machine vision technique, the prospect frame that is specifically related to be applied in the analysis of safety monitoring sequence of frames of video is judged.
Background technology
It is the technology that detects the foreground target that whether has motion or slowly mobile or short stay in the video that the prospect frame detects, at present in the video analysis process, widely used is that the less frame difference method of calculation cost and background method are (referring to Herrero S, Besc ó s J. Background Subtraction Techniques:Systematic Evaluation and Comparative Analysis[C] //Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems.Springer-Verlag. 2009,5807/2009:33-42).Frame difference method is responsive to noise ratio, is difficult for detecting the slowly mobile or temporary transient static foreground target of fixed area; And the background method is to the relative robust of noise, but influenced greatly by illumination variation, foreground target detects wayward, therefore, for addressing these problems, in recent years based on the algorithm of profile neighborhood information such as Snake model (referring to: Nie Xuan, Zhao Rongchun, Shen Yaping. the moving target profile based on the Snake technology extracts [J]. computer engineering. 2005,31 (23): 148-150), level set (referring to: Gong Yongyi, Luo Xiaonan, Huang Hui etc. the multiple goal profile based on single level set extracts [J]. Chinese journal of computers. 2007,30 (001): 120-128) etc., although it is better to be used for the foreground detection result, but the computation complexity height is difficult to reach live effect.
Summary of the invention
The objective of the invention is: overcome the slow even temporary transient defective of stagnating of speed that general foreground detection method can not adapt to variation of scene light and foreground moving target, a kind of simple, real-time prospect frame detection method based on edge model that calculates is provided.
Technical scheme of the present invention adopts following steps: (1) adopts under same fixedly camera position, the Same Scene picked-up to need that the prospect frame detects, resolution to be
Figure 2011101854157100002DEST_PATH_IMAGE002
The particular detection video, only contain the edge frame of edge image with pseudo-ball edge detection operator abstraction sequence frame; (2) current the of the described edge frame of statistics tThe frame border pixel ( I, j) at the frame sequence timing statistics
Figure 2011101854157100002DEST_PATH_IMAGE004
Interior probability of occurrence
Figure 2011101854157100002DEST_PATH_IMAGE006
,
Figure 2011101854157100002DEST_PATH_IMAGE008
Figure 2011101854157100002DEST_PATH_IMAGE010
Be kThe pairing binaryzation edge image of frame; (3) Rule of judgment of distinguishing according to current edge frame inward flange pixel attribute , Be tEdge pixel point on the pairing binaryzation edge image of frame, 0 is labeled as background attribute, and 1 is labeled as the prospect attribute,
Figure 2011101854157100002DEST_PATH_IMAGE016
Be background edge pixel decision threshold; (4) in the prospect edge image, if current the tCounting and be less than or equal to 2 in the edge that links to each other in the frame, then removes for noise spot; To the sum of all pixels of last prospect edge image less than sequence frame noise edge judgment threshold
Figure 2011101854157100002DEST_PATH_IMAGE018
, be judged to and be background frames, otherwise be the prospect frame.
The invention has the beneficial effects as follows;
1, the present invention is based on edge model the prospect frame detection method theoretical foundation be that the stability of edge in frame sequence of foreground target is far away not as good as background edge, for the foreground moving analysis provides necessary, a spot of motion pixel, significantly reduced calculation cost.
2, the present invention not only is suitable for non-rigid body target prospect frame and judges, is applicable to that also the prospect frame of rigid body target is judged, can adapt to effectively that background light changes and target travel is slow or situation such as of short duration delay, for follow-up target motion analysis creates favorable conditions.
Description of drawings
Below in conjunction with the drawings and specific embodiments the present invention is described in further detail.
Fig. 1 is the process flow diagram of prospect frame detection method of the present invention.
Embodiment
Further specify technical scheme of the present invention below in conjunction with accompanying drawing:
Referring to shown in Figure 1, at first, extract video sample, adopt the particular detection video that picked-up needs the prospect frame to detect under same fixedly camera position, the Same Scene, resolution
Figure 955731DEST_PATH_IMAGE002
Be 320 * 240, the implication of scene certain fixing geographic area of being meant shooting herein, different scene video parameter values is different, but under the Same Scene, the frame sequence timing statistics
Figure 341713DEST_PATH_IMAGE004
, background edge pixel decision threshold
Figure 121450DEST_PATH_IMAGE016
With sequence frame noise edge judgment threshold
Figure 985501DEST_PATH_IMAGE018
These three parameter values are stable.The particular detection video that needs are detected manually intercepts.Then, the particular detection sequence of frames of video that needs are detected adopts pseudo-ball operator edge detection operator to handle, and extracts resolution Be the edge image of 320 * 240 sequence frames, obtain only to contain the edge frame of edge image.
Before carrying out the detection of prospect frame, must obtain this three parameter values.
The frame sequence timing statistics
Figure 91177DEST_PATH_IMAGE004
Definite method be:
Comprise the of short duration delay of foreground target in the particular detection sequence of frames of video of artificial intercepting
Figure 2011101854157100002DEST_PATH_IMAGE020
Individual sample video-frequency band, and there are not the variation of scene light in these sample video-frequency bands is promptly chosen and is not contained that light changes but prospect sample video-frequency band that of short duration delay campaign can be arranged.Adopt instantaneous frame difference method (referring to Herrero S to each sample video-frequency band, Besc ó s J. Background Subtraction Techniques:Systematic Evaluation and Comparative Analysis[C] //Proceedings of the 11th International Conference on Advanced Concepts for Intelligent Vision Systems. Springer-Verlag. 2009,5807/2009:33-42) carry out motion detection, utilize instantaneous frame difference method can't discern the characteristics of static prospect, obtain temporary transient actionless foreground target by the time period of background absorption
Figure 2011101854157100002DEST_PATH_IMAGE022
As the hold-up time.The present frame foreground target area that after the consecutive frame difference, obtains
Figure 2011101854157100002DEST_PATH_IMAGE024
, it accounts for the two field picture total area Ratio Less than certain less threshold value
Figure 2011101854157100002DEST_PATH_IMAGE030
When (preferred 0.07), think that then prospect is detained or do not have a prospect, present frame attribute
Figure 2011101854157100002DEST_PATH_IMAGE032
Be labeled as 0, otherwise be 1, be i.e. Yun Dong prospect frame.Add up in every section video
Figure 681513DEST_PATH_IMAGE032
Be that 0 length is as the time period continuously
Figure 349255DEST_PATH_IMAGE022
In all video-frequency band samples, select the maximum time period
Figure 18134DEST_PATH_IMAGE022
As maximum time period
Figure 2011101854157100002DEST_PATH_IMAGE034
(preferred 440), see formula (1):
Figure 2011101854157100002DEST_PATH_IMAGE036
（1）
Test in theory
Figure 949181DEST_PATH_IMAGE020
Individual sample video hop count more better owing to can't obtain globally optimal solution, and there is local motion in non-rigid body target, it is very short really to be detained the actionless time, so
Figure 172352DEST_PATH_IMAGE020
Value depend on that whether comprising maximum prospect in the video-frequency band that subjective judgement intercepts is detained at interval, in the practice
Figure 378205DEST_PATH_IMAGE020
Desirable 5～10(preferred 10); And the existence of camera system electronic noise influences the stability that real scene is mapped to two dimensional image edge pixel point, so calculate maximum time period Need add a modified value again As final frame sequence timing statistics
Figure 767653DEST_PATH_IMAGE004
Value, see formula (2),
Figure 2011101854157100002DEST_PATH_IMAGE040
Relevant with camera system, the different video acquisition system of size of value, modified value
Figure 478120DEST_PATH_IMAGE038
Be inconsistent, general
Figure 222085DEST_PATH_IMAGE040
Value is 10～20, and is preferred Value is 16(20FPS).It also is final frame sequence timing statistics
Figure 669564DEST_PATH_IMAGE004
Be 456, the motionless state of stopping of non-rigid body foreground target in the expression scene, the longest less than 22.8 seconds.If foreground target is a rigid body, and it is in a single day static when just being counted as background, Can be made as 0.
Figure 2011101854157100002DEST_PATH_IMAGE042
（2）
Background edge pixel decision threshold
Figure 414983DEST_PATH_IMAGE016
Definite method be:
1 the sample video-frequency band that does not comprise foreground target and do not exist scene light to change in the particular video frequency frame sequence of artificial intercepting, adopt pseudo-ball edge detection operator (referring to Wang Zhiheng to the sample video-frequency band, Wu Fuchao. pseudo-ball filtering and rim detection [J]. the software journal. 2008,19 (4): the 803-816) edge image of the sequence frame of extraction video sample, the scale parameter of pseudo-herein ball operator
Figure 2011101854157100002DEST_PATH_IMAGE044
Get 3.0, the edge keeps parameter Get 0.1, template size is 5 * 5.At the frame sequence timing statistics Under the prerequisite of determining, the pixel that calculates on each frame border at the probability that same position occurs is
Figure 2011101854157100002DEST_PATH_IMAGE048
, establish when the tFrame background edge sum of all pixels is P, then in the Making by Probability Sets that each point occurs therein, get minimum threshold
Figure 2011101854157100002DEST_PATH_IMAGE050
For judging the threshold value of target context edge pixel, see formula (3):
(3)
For obtaining adaptability threshold value preferably, need from the tFrame begins follow-on test nFrame (being taken as 15 here) is up to T+nFrame, for avoiding exceptional value, nIndividual By ordering from small to large, get the median conduct
Figure 415672DEST_PATH_IMAGE016
(preferred 0.644) sees formula (4).
Figure 2011101854157100002DEST_PATH_IMAGE054
(4)
If video to be detected is current tThe edge pixel point of frame
Figure 2011101854157100002DEST_PATH_IMAGE056
Greater than
Figure 235860DEST_PATH_IMAGE016
The time, edge and removing as a setting then, remaining being comprises noise at interior prospect edge image.Different video scenes is suitable
Figure 555721DEST_PATH_IMAGE016
And it is inconsistent.
Sequence frame noise edge judgment threshold
Figure 967111DEST_PATH_IMAGE018
Definite method be:
Artificial intercepting frame number is from the sample video-frequency band
Figure 2011101854157100002DEST_PATH_IMAGE058
(preferred
Figure 2011101854157100002DEST_PATH_IMAGE060
Frame) the video-frequency band that does not contain prospect
Figure 2011101854157100002DEST_PATH_IMAGE062
(preferred
Figure 2011101854157100002DEST_PATH_IMAGE064
Volume), right
Figure 811570DEST_PATH_IMAGE062
Individual sample video-frequency band is respectively through the edge image of pseudo-ball edge detection operator abstraction sequence frame, the edge that keeps non-background, calculate the image that obtains through formula (8), formula (9) and be the noise image that does not contain prospect, removal is connected to 2 and reaches isolated noise edge pixel again, add up the remaining edge pixel number of spots of every frame at last, obtain
Figure 2011101854157100002DEST_PATH_IMAGE066
Individual discrete value, statistics the iFrame border pixel quantity
Figure 2011101854157100002DEST_PATH_IMAGE068
By ordering from small to large, noise edge pixel quantity statistical property meets Gaussian distribution, adopts median method approximate the iFrame border pixel quantity
Figure 169870DEST_PATH_IMAGE068
Expectation value
Figure 2011101854157100002DEST_PATH_IMAGE070
With Value is (preferred
Figure 192108DEST_PATH_IMAGE018
Value is 35), see formula (5) and formula (7).
（5）
Figure 2011101854157100002DEST_PATH_IMAGE074
（6）
Figure 2011101854157100002DEST_PATH_IMAGE076
（7）
Then, statistics frame sequence timing statistics
Figure 523863DEST_PATH_IMAGE004
The probability distribution of interior all edge pixels, the edge pixel point of promptly adding up edge frame is at the frame sequence timing statistics In probability of occurrence, establish present frame and be the tFrame is at the frame sequence timing statistics Under the prerequisite of determining,
Figure 31702DEST_PATH_IMAGE048
Be current tThe frame border pixel ( I, j) probability, computing formula is seen formula (8).
Figure 381912DEST_PATH_IMAGE006
，
Figure 409910DEST_PATH_IMAGE008
（8）
Carry out the judgement of prospect frame in the frame sequence according to formula (8), by background edge pixel decision threshold
Figure 558870DEST_PATH_IMAGE016
Extraction prospect edge image, the frame sequence timing statistics greater than
Figure 217384DEST_PATH_IMAGE004
Video-frequency band in carry out the sport foreground frame and judge, wherein,
Figure 586049DEST_PATH_IMAGE010
Be kThe pairing binaryzation edge image of frame.The probability of occurrence of background edge pixel in theory
Figure 152159DEST_PATH_IMAGE048
Inevitable greater than active margin pixel probability, promptly at the frame sequence timing statistics
Figure 922669DEST_PATH_IMAGE004
In, if greater than background edge pixel decision threshold
Figure 752085DEST_PATH_IMAGE016
, this edge pixel point
Figure 2011101854157100002DEST_PATH_IMAGE078
Be labeled as 0, show frame border On time of occurring in same position of point long belong to background; Otherwise be labeled as 1, show that the time ratio of appearance is the activity prospect than what lack, see formula (9).
（9）
Reservation element marking value is 1 pixel in edge image, is the prospect edge image of extraction.
At last, in described prospect edge image, according to sequence frame noise edge Rule of judgment, the elimination noise pixel obtains the foreground target edge of frame.Whether be noise spot, if the edge pixel that links to each other in the present frame is counted according to neighborhood information if analyzing each prospect edge pixel point
Figure 2011101854157100002DEST_PATH_IMAGE080
Be less than or equal to 2, then be considered as noise spot and remove; But still exist continuous counting to surpass 2 noise possibility in theory, thus last prospect edge pixel sum is compared with sequence frame noise edge judgment threshold, to last prospect edge pixel sum threshold value
Figure 773502DEST_PATH_IMAGE018
Judge that this frame detects and is background frames when being worth less than this, otherwise is the prospect frame, keeps the prospect frame.
Even the present invention's scene light changes and foreground target speed is slow even time-out, do not influence the detection of prospect frame yet, when detecting present frame when being the prospect frame, do the time spent as the monitoring security protection and can send early warning, or when analyzing, for subsequent act research provides a spot of, necessary foreground target information as foreground moving.

Claims (4)

1. prospect frame detection method based on edge model is characterized in that adopting following steps:
(1) adopt under same fixedly camera position, the Same Scene picked-up to need that the prospect frame detects, resolution to be The particular detection video, only contain the edge frame of edge image with pseudo-ball edge detection operator abstraction sequence frame;
(2) current the of the described edge frame of statistics tThe frame border pixel ( I, j) at the frame sequence timing statistics
Figure 2011101854157100001DEST_PATH_IMAGE004
Interior probability of occurrence
Figure 2011101854157100001DEST_PATH_IMAGE006
,
Figure 2011101854157100001DEST_PATH_IMAGE008
Figure 2011101854157100001DEST_PATH_IMAGE010
Be kThe pairing binaryzation edge image of frame;
(3) Rule of judgment of distinguishing according to current edge frame inward flange pixel attribute
Figure 2011101854157100001DEST_PATH_IMAGE012
,
Figure 2011101854157100001DEST_PATH_IMAGE014
Be tEdge pixel point on the pairing binaryzation edge image of frame, 0 is labeled as background attribute, and 1 is labeled as the prospect attribute,
Figure 2011101854157100001DEST_PATH_IMAGE016
Be background edge pixel decision threshold;
(4) in the prospect edge image, if current the tCounting and be less than or equal to 2 in the edge that links to each other in the frame, then removes for noise spot; To the sum of all pixels of last prospect edge image less than sequence frame noise edge judgment threshold
Figure 2011101854157100001DEST_PATH_IMAGE018
, be judged to and be background frames, otherwise be the prospect frame.
2. a kind of prospect frame detection method based on edge model according to claim 1 is characterized in that: the described frame sequence timing statistics of step (2)
Figure 97184DEST_PATH_IMAGE004
Determine by the following method:
1) manually intercepts and comprise the of short duration delay of foreground target in the particular video frequency frame sequence and do not exist scene light to change
Figure 2011101854157100001DEST_PATH_IMAGE020
Individual sample video-frequency band;
2) right
Figure 174861DEST_PATH_IMAGE020
Individual sample video-frequency band is obtained temporary transient actionless foreground target frame number through instantaneous frame difference method respectively
Figure 2011101854157100001DEST_PATH_IMAGE022
As the hold-up time, the selection maximum
Figure 315730DEST_PATH_IMAGE022
As
Figure 2011101854157100001DEST_PATH_IMAGE024
3) frame sequence timing statistics
Figure 2011101854157100001DEST_PATH_IMAGE026
, wherein
Figure 2011101854157100001DEST_PATH_IMAGE028
, Be the modified value relevant with camera system.
3. a kind of prospect frame detection method based on edge model according to claim 1 is characterized in that: the described background edge pixel of step (3) decision threshold
Figure 732936DEST_PATH_IMAGE016
Determine by the following method:
1) manually intercepts 1 the sample video-frequency band that does not comprise foreground target in the particular video frequency frame sequence and do not exist scene light to change;
2) to the edge image of sample video-frequency band, at the frame sequence timing statistics through pseudo-ball edge detection operator abstraction sequence frame
Figure 169734DEST_PATH_IMAGE004
Under the prerequisite of determining, the pixel that calculates on each frame border at the probability that same position occurs is , when tFrame background edge sum of all pixels is P, get the probability minimum value
Figure 2011101854157100001DEST_PATH_IMAGE034
3) from tFrame begins continuously extremely nFrame, nIndividual
Figure 2011101854157100001DEST_PATH_IMAGE036
By ordering from small to large, median is a background edge pixel decision threshold
Figure 2011101854157100001DEST_PATH_IMAGE038
4. a kind of prospect frame detection method based on edge model according to claim 1 is characterized in that: the described sequence frame noise edge of step (4) judgment threshold
Figure 672390DEST_PATH_IMAGE018
Determine by the following method:
1) manually intercepting the frame number that does not comprise prospect in the particular video frequency frame sequence is
Figure 2011101854157100001DEST_PATH_IMAGE040
Individual sample video-frequency band is right
Figure 820213DEST_PATH_IMAGE042
Individual sample video-frequency band keeps the edge of non-background respectively through the edge image of pseudo-ball edge detection operator abstraction sequence frame;
2) the remaining edge pixel number of spots of the every frame of statistics, the iFrame border pixel quantity
Figure 2011101854157100001DEST_PATH_IMAGE044
By ordering from small to large, obtain Individual discrete value;
3) determine sequence frame noise edge judgment threshold
Figure 2011101854157100001DEST_PATH_IMAGE048
, wherein:
Figure 2011101854157100001DEST_PATH_IMAGE050
,
Figure 2011101854157100001DEST_PATH_IMAGE052
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