CN101582160B - Foreground detection method and device as well as adaptive threshold adjusting method and device - Google Patents
Foreground detection method and device as well as adaptive threshold adjusting method and device Download PDFInfo
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Abstract
The invention discloses a foreground detection method and a device as well as an adaptive threshold adjusting method and a device. The foreground detection method comprises the following steps: comparing a current input image with a background image of each frame to obtain the difference value points respectively corresponding to pixel points of the current input image; carrying out statistics onthe percentage of the difference value point number, with value more than different thresholds and choosing a threshold suitable for the current input image according to the variation trend that the percentage of the difference value point increases along with the threshold value. Thus, when foreground detection is carried out, the threshold can be adjusted aiming at the monitoring scene with highor low noise, so that the adjusted threshold can be used for judging the foreground pixel points in the image, thereby improving the accuracy of foreground detection and further improving the accuracy of moving object detection and tracking.
Description
Technical field
The present invention relates to the foreground detection technology, a kind of adaptive threshold control method and the device that particularly can be used for a kind of foreground detection method and the device of moving object detection and tracking and can be used for foreground detection.
Background technology
In the existing video monitoring apparatus, normally utilize static camera to photograph video in the monitoring scene, then the continuous multiple frames image of this video carried out the moving object detection and tracking, so that the moving object that is different from background image in the continuous multiple frames image is analyzed.
In the detection and tracking of moving object, foreground detection is first step, and its order of accuarcy directly has influence on the performance of whole device.The processing procedure of existing foreground detection is as follows: first current input image and background image are compared, obtain in the current input image the poor of the value of respective pixel in each pixel and background image, the difference that obtains value can be referred to as the difference point, a pixel in the corresponding current input image of each difference point difference; Then the threshold value of each difference point with predefined corresponding specific image noise level compared, and will greater than or more than or equal to difference point corresponding pixel in current input image of this threshold value be defined as the foreground pixel point, will less than or equal to or be defined as the background pixel point less than difference point corresponding pixel in current input image of this threshold value.After this, all foreground pixel points are carried out the prospect clustering processing, each foreground area that can obtain being consisted of by different foreground pixel points.
In the practical application, during the environment generation climate change of monitoring scene place, can cause the height of noise in the monitoring scene to change, and should set larger threshold value, should set less threshold value for the less monitoring scene of noise for the larger monitoring scene of noise.Yet employed threshold value but all is to preset and changeless in the existing foreground detection, can't change with the height of noise in the monitoring scene and dynamically adjust, and this just might reduce the accuracy of foreground detection.
For example, when fine and rainy day or snow day, because the interference in intensity of illumination and the picture has obvious difference, the threshold value of setting for fine day so may just be not suitable for for rainy day or snow sky in the monitoring scene; In like manner, use infrared photosensitive sensor for the color sensor of visible light and at night because camera uses by day, thereby may just be not suitable for for night for the threshold value of setting daytime.
As seen, for noise or high or low any monitoring scene, existing motion detection is merely able to judge foreground pixel point in the image with changeless threshold value, thus meeting so that the accuracy of foreground detection is not high, and then meeting is so that the accuracy of moving object detection and tracking is not high.
Summary of the invention
In view of this, the invention provides a kind of foreground detection method and device and a kind of adaptive threshold control method and device, can be for the employed threshold value of noise level dynamic adjustments foreground detection in the image.
A kind of foreground detection method provided by the invention comprises:
A1, current input image and background image are compared, obtain and each pixel of current input image corresponding difference point respectively;
A2, all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtain respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, n is the positive integer greater than 1;
A3, in the threshold interval of threshold value i～threshold value j, any selected threshold value; Wherein, the value of the number percent that step a2 obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
A4, the selected threshold decision of utilization go out the foreground pixel point in the current input image.
Before described step a1, the method further comprises:
A0, to carrying out walkaway with exist together some frame test patterns of a monitoring scene of described current input image, if the noise level that detects reaches predetermined degree, then first described current input image is carried out picture smooth treatment, then the current input image after the picture smooth treatment is carried out described step a1; Otherwise, directly carry out described step a1.
Described step a0 comprises:
A01, each frame and background image in some frame test patterns are compared, obtain and each pixel of each frame test pattern corresponding difference point respectively;
A02, each difference point of each frame test pattern is compared with a upper limit threshold and a lower limit threshold value respectively, and statistics obtains in each difference point of each frame test pattern, value is respectively greater than the difference point quantity of upper limit threshold and lower threshold;
A03, calculate poor greater than the difference point quantity of the difference point quantity of lower threshold and lower threshold of each frame test pattern, if the mean value of the described difference that each frame test pattern is corresponding reaches the predetermined value that an expression noise level reaches predetermined extent, then first described current input image is carried out picture smooth treatment, then the current input image after the picture smooth treatment is carried out described step a1; Otherwise, directly carry out described step a1.
Described step a3 comprises:
The number percent value that a31, the value that increases progressively take threshold value 1～threshold value n obtain as horizontal ordinate, step a2 is as ordinate, the number percent curve that establishment step a2 obtains;
A32, obtain described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
A33, in described slope curve, selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The average D of the slope value in the threshold interval that a34, calculating are selected
AvgAnd variances sigma;
A35, according to the resulting average D of step a34
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
A36, in described slope curve, from slope value all threshold values less than step a35 acquired results of correspondence, selected value is near of threshold value i.
After the described step a3, before the step a4, the method further comprises: a3 ', be utilized as former frame or the selected threshold value of multiframe, the threshold value that step a3 is selected is carried out smoothing processing;
Described step a4 utilizes the threshold decision after the smoothing processing to go out foreground pixel point in the current input image.
Described step a3 ' is utilized as the selected threshold value of former frame the selected threshold value of step a3 is carried out smoothing processing according to following formula:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1To be the selected threshold value of former frame, T
kFor the selected threshold value of step a3, k are the positive integer greater than 1.
A kind of foreground detection device provided by the invention comprises:
Difference point acquiring unit is used for current input image and background image are compared, and obtains the difference point corresponding with each pixel difference of current input image;
The number percent acquiring unit is used for all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtains respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, and n is the positive integer greater than 1;
The threshold selection unit is used in the threshold interval of threshold value i～threshold value j, arbitrarily selected threshold value; Wherein, the value of the number percent that the number percent acquiring unit obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
The prospect judging unit is used for utilizing selected threshold decision to go out the foreground pixel point of current input image.
This device further comprises level and smooth decision unit, be used for carrying out walkaway with exist together some frame test patterns of a monitoring scene of described current input image, if the noise level that detects reaches predetermined degree, then first described current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to described difference point acquiring unit; Otherwise, directly described current input image is exported to described difference point acquiring unit.
Described level and smooth decision unit comprises:
Difference statistics subelement is used for each frame and the background image of some frame test patterns are compared, and obtains the difference point corresponding with each pixel difference of each frame test pattern;
The quantity statistics subelement, be used for each difference point of each frame test pattern is compared with a upper limit threshold and a lower limit threshold value respectively, and statistics obtains in each difference point of each frame test pattern, and value is respectively greater than the difference point quantity of upper limit threshold and lower threshold;
The enforcement of the judgment subelement, be used for to calculate poor greater than the difference point quantity of the difference point quantity of lower threshold and lower threshold of each frame test pattern, if the mean value of the described difference that each frame test pattern is corresponding reaches the predetermined value that an expression noise level reaches predetermined extent, then first described current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to described difference point acquiring unit; Otherwise, directly described current input image is exported to described difference point acquiring unit.
Described threshold selection unit comprises:
Curve is set up subelement, is used for number percent value that the value that increases progressively take threshold value 1～threshold value n obtains as horizontal ordinate, number percent acquiring unit as ordinate, sets up the number percent curve that the number percent acquiring unit obtains;
Slope obtains subelement, be used for obtaining described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
Interval selected subelement is used at described slope curve, and selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The first computation subunit is for the average D that calculates the slope value in the selected threshold interval
AvgAnd variances sigma;
The second computation subunit, the average D that foundation the first computation subunit obtains
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
Relatively choose subelement, be used at described slope curve, from slope value all threshold values less than the second computation subunit acquired results of correspondence, selected value is near of threshold value i.
This device further comprises the threshold smoothing unit between described threshold selection unit and described prospect judging unit, be used for being utilized as former frame or the selected threshold value of multiframe, and the threshold value that the threshold selection unit is selected is carried out smoothing processing;
And described prospect judging unit utilizes the threshold decision after the smoothing processing to go out foreground pixel point in the current input image.
Described threshold smoothing unit comprises:
The threshold value storing sub-units is used for being stored as the selected threshold value of former frame;
The level and smooth subelement of carrying out is used for according to following formula, is utilized as the selected threshold value of former frame the selected threshold value in threshold selection unit is carried out smoothing processing:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1To be the selected threshold value of former frame, T
kFor the threshold selection unit is that the selected threshold value of current input image, k are the positive integer greater than 1.
A kind of adaptive threshold control method provided by the invention comprises:
A1, current input image and background image are compared, obtain and each pixel of current input image corresponding difference point respectively;
A2, all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtain respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, n is the positive integer greater than 1;
The threshold interval of a3, selected threshold value i～threshold value j, and in this selected threshold interval, select arbitrarily a threshold value; Wherein, the value of the number percent that step a2 obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n.
Described step a3 comprises:
The number percent value that a31, the value that increases progressively take threshold value 1～threshold value n obtain as horizontal ordinate, step a2 is as ordinate, the number percent curve that establishment step a2 obtains;
A32, obtain described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
A33, in described slope curve, selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The average D of the slope value in the threshold interval that a34, calculating are selected
AvgAnd variances sigma;
A35, according to the resulting average D of step a34
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
A36, in described slope curve, from slope value all threshold values less than step a35 acquired results of correspondence, selected value is near of threshold value i.
After the described step a3, the method further comprises: a3 ', be utilized as former frame or the selected threshold value of multiframe, the threshold value that step a3 is selected is carried out smoothing processing.
Described step a3 ' is utilized as the selected threshold value of former frame the selected threshold value of step a3 is carried out smoothing processing according to following formula:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1To be the selected threshold value of former frame, T
kFor the selected threshold value of step a3, k are the positive integer greater than 1.
A kind of adaptive thresholding value adjusting device provided by the invention comprises:
Difference point acquiring unit is used for current input image and background image are compared, and obtains the difference point corresponding with each pixel difference of current input image;
The number percent acquiring unit is used for all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtains respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, and n is the positive integer greater than 1;
The threshold selection unit is used for the threshold interval of selected threshold value i～threshold value j, and selectes arbitrarily a threshold value in this selected threshold interval; Wherein, the value of the number percent that the number percent acquiring unit obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n.
Described threshold selection unit comprises:
Curve is set up subelement, is used for number percent value that the value that increases progressively take threshold value 1～threshold value n obtains as horizontal ordinate, number percent acquiring unit as ordinate, sets up the number percent curve that the number percent acquiring unit obtains;
Slope obtains subelement, be used for obtaining described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
Interval selected subelement is used at described slope curve, and selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The first computation subunit is for the average D that calculates the slope value in the selected threshold interval
AvgAnd variances sigma;
The second computation subunit, the average D that foundation the first computation subunit obtains
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
Relatively choose subelement, be used at described slope curve, from slope value all threshold values less than the second computation subunit acquired results of correspondence, selected value is near of threshold value i.
This device further comprises:
The threshold smoothing unit is used for being utilized as former frame or the selected threshold value of multiframe, and the threshold value that the threshold selection unit is selected is carried out smoothing processing.
Described threshold smoothing unit comprises:
The threshold value storing sub-units is used for being stored as the selected threshold value of former frame;
The level and smooth subelement of carrying out is used for according to following formula, is utilized as the selected threshold value of former frame the selected threshold value in threshold selection unit is carried out smoothing processing:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1To be the selected threshold value of former frame, T
kFor the threshold selection unit is that the selected threshold value of current input image, k are the positive integer greater than 1.
As seen from the above technical solution, the present invention can compare each frame current input image and background image, obtain the difference point corresponding with each pixel difference of current input image, then add up value greater than the number percent of the difference point quantity of different threshold values, and according to the size of difference point number percent along with the variation tendency that the threshold value value increases, choose a threshold value that is suitable for current input image.Like this, when carrying out foreground detection, can regulate threshold value for noise or high or low monitoring scene, thereby can judge foreground pixel point in the image with the threshold value after regulating, thereby improve the accuracy of foreground detection, and then can improve the accuracy of moving object detection and tracking.
Further, the present invention also can judge first noise intensity overall in the monitoring scene, if the noise level that detects reaches predetermined degree, noise overall in the expression monitoring scene is stronger, then start the picture smooth treatment to follow-up each two field picture for the moving object detection and tracking, to reduce noise, particularly slight jitter when occuring in video, but adopt noise, the reduce disturbance of picture smooth treatment decrease image, thereby further improve the accuracy of foreground detection, and then also can further improve the accuracy of moving object detection and tracking.
Description of drawings
Fig. 1 is the schematic flow sheet of detection noise level in the embodiment of the invention foreground detection method;
Fig. 2 is the schematic flow sheet that threshold value is regulated in the embodiment of the invention foreground detection method;
Fig. 3 is the schematic flow sheet of threshold selection process in the flow process as shown in Figure 2;
The number percent curve synoptic diagram of Fig. 4 for setting up in the threshold selection process as shown in Figure 3;
The slope curve schematic diagram of Fig. 5 for setting up in the threshold selection process as shown in Figure 3;
Fig. 6 is the structural representation of foreground detection device in the embodiment of the invention;
Fig. 7 is the structural representation in the foreground detection device as shown in Figure 6;
Fig. 8 is the structural representation in the foreground detection device as shown in Figure 6.
Embodiment
For making purpose of the present invention, technical scheme and advantage clearer, referring to the accompanying drawing embodiment that develops simultaneously, the present invention is described in more detail.
Be applied to the moving object detection and tracking as example take foreground detection, in an embodiment, foreground detection method can be mainly two parts:
Pre-service before setting in motion object detection and tracking: the some two field pictures to monitoring scene carry out walkaway, namely judge first noise intensity overall in the monitoring scene, if the noise level that detects reaches predetermined degree, noise overall in the expression monitoring scene is stronger, then start the picture smooth treatment to follow-up each two field picture for the moving object detection and tracking, to reduce noise, particularly slight jitter is when occuring in video, but adopts noise, the reduce disturbance of picture smooth treatment decrease image;
Regulate in the threshold value of setting in motion object detection when following the tracks of: preset the different threshold value of a plurality of values, then concerning each frame current input image, current input image and background image are compared, after this, again respectively for the above-mentioned different threshold value of a plurality of values that presets, statistics accounts for the number percent of difference point sum in the current input image greater than the difference point quantity of each threshold value, then according to the size of difference point number percent along with the variation tendency that the threshold value value increases, choose a threshold value that is suitable for current input image and be used for foreground detection.
In above-mentioned two parts, the pretreated front portion of moving object detection and tracking is optional and nonessential.If foreground detection is applied to other field, need not especially to carry out the pretreated front portion of moving object detection and tracking.
In addition, need to prove, this paper in full mentioned background image can be long-term background and/or short-term background.Wherein, for long-term background and the short-term background situation of image as a setting, suppose the value B that difference point that A is ordered has corresponding long-term background
A_long(k) and corresponding short-term background another the value B
A_short(k), then the value of definite this difference point is:
Min[|B
A_long(k)-I
A(k) |, | B
A_short(k)-I
A(k) |], I
A(k) be the pixel value of A point in input picture.
Below, the above-mentioned two-part concrete scheme in the present embodiment foreground detection method is elaborated.
1) be used for the preprocessing part of moving object detection and tracking in the foreground detection method of the present embodiment:
The interference of the many factors such as camera head imaging noise, sharpness, DE Camera Shake, in the input picture of the continuous multiple frames that camera head gathers, the pixel value of same position background dot can constantly change, if directly this type of input picture is carried out foreground detection, then a lot of noise region mistakes can be identified as foreground area, thereby affect the accuracy of moving object detection and tracking.
But because variation range and the amplitude of above-mentioned background point are all very little, as long as input picture is carried out smoothing processing, just but the noise of decrease input picture is also eliminated the float in the continuous multiple frames input picture simultaneously, can not cause the appearance of much noise object.But, then can cause negative effect if the smaller input picture of noise is carried out picture smooth treatment, for example the easy smoothed background that is treated to of some smaller object causes this object to be detected and to follow the tracks of.
Therefore, just need to carry out walkaway to some frame test patterns, above-mentioned some frame test patterns and subsequent motion object detection and employed each the frame current input image of tracking belong to same monitoring scene, if the noise level that detects reaches predetermined degree, then when setting in motion object detection and tracking, startup is to the picture smooth treatment of each frame current input image, so that each frame current input image is carried out foreground detection again through after the picture smooth treatment; Otherwise, when setting in motion object detection and tracking, directly each frame current input image is carried out foreground detection.
More specifically, the present embodiment provides a kind of mode of new detection noise level.
Fig. 1 is the schematic flow sheet of detection noise level in the embodiment of the invention foreground detection method.The flow process of detection noise level as shown in Figure 1 for some frame test patterns, and comprises the steps:
Step 101 compares each frame and background image in some frame test patterns, obtains the difference point corresponding with each pixel difference of each frame test pattern.
Step 102 compares each difference point of each frame test pattern respectively with a upper limit threshold and a lower limit threshold value, and statistics obtains value in each difference point of each frame test pattern respectively greater than the difference point quantity of upper limit threshold and lower threshold.
Need to prove, for the less lower threshold of value, usually comprise noise spot and foreground point greater than the difference point of this lower threshold; And for the larger upper limit threshold of value, almost only comprise the foreground point greater than the difference point of this upper limit threshold.
For example,, wherein except the foreground point, must also comprise owing to taking the system noise point that the camera head of this image causes take white as background and comprise the image of prospect for a width of cloth.So, this image and the plain white background image ratio that does not comprise system noise point obtained difference point after, in each the difference point greater than lower threshold, except real foreground point, also comprise the system noise point; And because the gray-scale value of system noise point usually can be less than the foreground point, thereby just only comprise the foreground point greater than possibility in each difference point of upper limit threshold.
Upper limit threshold can be described as prospect threshold value T
Fore, lower threshold can be described as noise threshold T
NoiseCorrespondingly, according to empirical law, in all difference points corresponding to each frame test pattern, greater than noise threshold T
NoiseDifference point quantity N (k, T
Noise) and greater than prospect threshold value T
ForeDifference point quantity N (k, T
Fore) poor, be N (k, T
Noise)-N (k, T
Fore) can regard as noise spot quantity, and available noise point quantity characterizes the noise level of this frame test pattern.Preferably, prospect threshold value T
ForeDesirable 24, the noise threshold T of empirical value
NoiseEmpirical value desirable 8.
Based on above-mentioned empirical law, step 103, calculate poor greater than the difference point quantity of the difference point quantity of lower threshold and lower threshold of each frame test pattern, if the mean value of the described difference that each frame test pattern is corresponding reaches a predetermined value, judge that then noise intensity overall in the monitoring scene reaches predetermined degree.
So far, this flow process finishes.
Certainly, the detection noise level can also realize according to existing mode in the present embodiment.
2) threshold value in the foreground detection method of the present embodiment is regulated part:
Set in advance threshold value 1～threshold value n that value increases progressively, i is more than or equal to 1 and less than n, and j is greater than i and less than or equal to n.
Fig. 2 is the schematic flow sheet that threshold value is regulated in the embodiment of the invention foreground detection method.Threshold value adjustment process as shown in Figure 2, all carry out following steps for each frame current input image:
Step 201 compares current input image and background image, obtains the difference point corresponding with each pixel difference of current input image.
Current input image in this step can be the current input image after picture smooth treatment, also can be the current input image without picture smooth treatment.
The resulting number percent of this step also just equals the ratio of all pixel quantity in difference point quantity and the current input image, the ratio that expression is counted greater than the difference of each among threshold value 1～threshold value n respectively.
Step 203 in the threshold interval of threshold value i～threshold value j, is selected arbitrarily a threshold value; Wherein, the value of the number percent that step 202 obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n.
The threshold interval of the threshold value place threshold value i that selectes in this step～threshold value j also is based on foregoing empirical law and selectes, and is described as follows:
The value of threshold value 1 is minimum, thereby in all difference points corresponding to current input image, and value has comprised noise spot and all foreground points all in the current input image basically greater than the difference point of threshold value 1.And along with the increasing progressively of threshold value value, can foreground point quantity then substantially can not change thereupon successively decreasing greater than the noise spot quantity in this and the threshold difference point quantity.Thus, when the value of number percent in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, namely mean greater than substantially not comprising noise spot in each the difference point among threshold value i～threshold value n, but greater than then might only comprising the part foreground point in each the difference point among threshold value j+1～threshold value n, thereby for so that neither comprise noise spot in the difference point greater than selected threshold value, can comprise all foreground points again, just should in the threshold interval of threshold value i～threshold value j, select arbitrarily a threshold value.
After this, the threshold decision that can utilize step 203 to select goes out the foreground pixel point in the current input image.
So far, this flow process finishes.
In above-mentioned flow process, the process of the selected threshold value of step 203 can realize based on setting up number percent curve and this number percent slope of a curve curve.As shown in Figure 3, the process of the selected threshold value of step 203 can specifically comprise in the above-mentioned flow process:
The number percent curve of setting up in this step can be referring to Fig. 4.In Fig. 4, T represents the value that threshold value 1～threshold value n increases progressively, and supposes that T gets 1～100; R (k, T) expression step 202 obtain respectively greater than number percent value, the 1≤k≤n of threshold value 1～threshold value n.
The number percent slope of a curve that step 2032, obtaining step 2031 are set up, and the value that still increases progressively take threshold value 1～threshold value n is as horizontal ordinate makes up the slope curve D (k, T) of the slope that obtains～T,
The slope curve of setting up in this step can be referring to Fig. 5.In Fig. 5, T represents value that threshold value 1～threshold value n increases progressively, supposes that T gets 1～100; D (k, T) expression step 202 obtain respectively greater than number percent value, the 1≤k≤n of threshold value 1～threshold value n.
Step 2034, the average D of the slope value in the threshold interval of threshold value 1～threshold value i-1 that calculating is selected
AvgAnd variances sigma.
Threshold value value selected in Fig. 5 is 21.
So far, this flow process finishes.
In the practical application, can be too inviolent in order to ensure changing for the selected threshold value value of each frame current input image, can after step 203, further be utilized as former frame or the selected threshold value of multiframe input picture in the present embodiment, the threshold value that step 203 is selected is carried out smoothing processing.Concrete threshold smoothing processing mode can be expressed as formula:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1To be the selected threshold value of former frame input picture, T
kFor the selected threshold value of step 203, k are the positive integer greater than 1.
Below, again the foreground detection device in the present embodiment is elaborated.
Fig. 6 is the structural representation of foreground detection device in the embodiment of the invention.As shown in Figure 6, the foreground detection device in the present embodiment comprises: level and smooth decision unit 600, difference point acquiring unit 601, number percent acquiring unit 602, threshold selection unit 603, prospect judging unit 604.
Level and smooth decision unit 600 is used for before the moving object detection and tracking begin some frame test patterns being carried out walkaway, and employed each the frame current input image of some frame test patterns and foreground detection belongs to same monitoring scene; If the noise level that detects reaches predetermined degree, then in the setting in motion object detection with after following the tracks of, first current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to difference point acquiring unit 601; Otherwise, directly current input image is exported to difference point acquiring unit 601.
Certainly, level and smooth decision unit 600 is for optional nonessential, and correspondingly, difference point acquiring unit 601 is not can only receive current input image by level and smooth decision unit 600 but can directly receive current input image from camera head just yet.
Difference point acquiring unit 601 is used for and will compares from current input image and the background image of level and smooth decision unit 600, obtains the difference point corresponding with each pixel difference of current input image.
Number percent acquiring unit 602, being used for the difference point that current input image is corresponding compares with threshold value 1～threshold value n that value increases progressively successively, obtain respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, n is the positive integer greater than 1.
Preferably, as shown in Figure 7, level and smooth decision unit 600 can comprise:
Difference statistics subelement 6001 is used for each frame and the background image of some frame test patterns are compared, and obtains the difference point corresponding with each pixel difference of each frame test pattern;
Quantity statistics subelement 6002, be used for each difference point of each frame test pattern is compared with a upper limit threshold and a lower limit threshold value respectively, and statistics obtains in each difference point of each frame test pattern, and value is respectively greater than the difference point quantity of upper limit threshold and lower threshold;
Enforcement of the judgment subelement 6003, be used for to calculate poor greater than the difference point quantity of the difference point quantity of lower threshold and lower threshold of each frame test pattern, if the mean value of the described difference that each frame test pattern is corresponding reaches the predetermined value that an expression noise level reaches predetermined extent, then first described current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to described difference point acquiring unit 601; Otherwise, directly described current input image is exported to described difference point acquiring unit 601.
Wherein, the foundation that enforcement of the judgment subelement 6003 is carried out its judgement is such as the described empirical law of this paper method part, does not repeat them here.
Preferably, as shown in Figure 8, the processing procedure that threshold selection unit 603 is carried out also can be according to for such as the described empirical law of this paper method part and comprise:
Curve is set up subelement 6031, is used for number percent value that the value that increases progressively take threshold value 1～threshold value n obtains as horizontal ordinate, number percent acquiring unit as ordinate, sets up the number percent curve that the number percent acquiring unit obtains;
Slope obtains subelement 6032, be used for obtaining described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up the slope curve of the value of the slope that obtains as horizontal ordinate;
Interval selected subelement 6033 is used at described slope curve, and selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The first computation subunit 6034 is for the average D that calculates the slope value in the selected threshold interval
AvgAnd variances sigma;
The second computation subunit 6035, the average D that foundation the first computation subunit 6034 obtains
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
Relatively choose subelement 6036, be used at described slope curve, from slope value all threshold values less than the second computation subunit 6035 acquired results of correspondence, selected value is near of threshold value i.
Still referring to Fig. 6, in the practical application, can be too inviolent in order to ensure changing for the selected threshold value value of each frame current input image, foreground detection device in the present embodiment is between threshold selection unit 603 and prospect judging unit 604, also can further comprise threshold smoothing unit 605, be used for being utilized as former frame or the selected threshold value of multiframe, threshold selection unit 603 selected threshold values are carried out smoothing processing.Correspondingly, prospect judging unit 604 utilizes threshold decision after the smoothing processing to go out foreground pixel point in the current input image.
Specifically, threshold smoothing unit 605 can comprise (not shown):
The threshold value storing sub-units is used for being stored as the selected threshold value of former frame;
The level and smooth subelement of carrying out is used for according to following formula, is utilized as the selected threshold value of former frame threshold selection unit 603 selected threshold values are carried out smoothing processing:
T
k’＝(1-β)T
k-1+βT
k；
Wherein, T
k' be threshold value, the weight of β Set arbitrarily, the T after the smoothing processing
K-1That threshold selection unit 603 is selected threshold value, the T of former frame
kFor threshold selection unit 603 is that the selected threshold value of present frame, k are the positive integer greater than 1.
Need to prove, difference point acquiring unit 601, number percent acquiring unit 602, threshold selection unit 603 can consist of a threshold value regulating device, and can be applicable to other purposes except foreground detection.And this threshold value regulating device also can further comprise threshold smoothing unit 605.
The above is preferred embodiment of the present invention only, is not for limiting protection scope of the present invention.Within the spirit and principles in the present invention all, any modification of doing, be equal to and replace and improvement etc., all should be included within protection scope of the present invention.
Claims (16)
1. a foreground detection method is characterized in that, the method comprises:
A1, current input image and background image are compared, obtain and each pixel of current input image corresponding difference point respectively;
A2, all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtain respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, n is the positive integer greater than 1;
A3, in the threshold interval of threshold value i～threshold value j, any selected threshold value; Wherein, the value of the number percent that step a2 obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
A4, the selected threshold decision of utilization go out the foreground pixel point in the current input image;
Wherein, described step a3 comprises:
The number percent value that a31, the value that increases progressively take threshold value 1～threshold value n obtain as horizontal ordinate, step a2 is set up the number percent curve as ordinate;
A32, obtain described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
A33, in described slope curve, selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The average D of the slope value in the threshold interval of threshold value 1～threshold value i-1 that a34, calculating are selected
AvgAnd variances sigma;
A35, according to the resulting average D of step a34
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
A36, in described slope curve, from slope value all threshold values less than step a35 acquired results of correspondence, selected value is near of threshold value i.
2. foreground detection method as claimed in claim 1 is characterized in that, before described step a1, the method further comprises:
A0, to carrying out walkaway with exist together some frame test patterns of a monitoring scene of described current input image, if the noise level that detects reaches predetermined degree, then first described current input image is carried out picture smooth treatment, then the current input image after the picture smooth treatment is carried out described step a1; Otherwise, directly carry out described step a1.
3. foreground detection method as claimed in claim 2 is characterized in that, described step a0 comprises:
A01, each frame and background image in some frame test patterns are compared, obtain and each pixel of each frame test pattern corresponding difference point respectively;
A02, each difference point of each frame test pattern is compared with a upper limit threshold and a lower limit threshold value respectively, and statistics obtains in each difference point of each frame test pattern, value is respectively greater than the difference point quantity of upper limit threshold and lower threshold;
A03, calculate each frame test pattern greater than poor with greater than the difference point quantity of upper limit threshold of the difference point quantity of lower threshold, if the mean value of the described difference that each frame test pattern is corresponding reaches the predetermined value that an expression noise level reaches predetermined extent, then first described current input image is carried out picture smooth treatment, then the current input image after the picture smooth treatment is carried out described step a1; Otherwise, directly carry out described step a1.
4. foreground detection method as claimed in claim 1 is characterized in that, after the described step a3, before the step a4, the method further comprises: a3 ', be utilized as former frame or the selected threshold value of multiframe, the threshold value that step a3 is selected is carried out smoothing processing;
Described step a4 utilizes the threshold decision after the smoothing processing to go out foreground pixel point in the current input image.
5. foreground detection method as claimed in claim 4 is characterized in that, described step a3 ' is utilized as the selected threshold value of former frame the selected threshold value of step a3 is carried out smoothing processing according to following formula:
T
k’=（1-β）T
k-1+βT
k；
Wherein, T
k' be the threshold value after the smoothing processing, weight, the T that β is Set arbitrarily
K-1To be the selected threshold value of former frame, T
kFor the selected threshold value of step a3, k are the positive integer greater than 1.
6. a foreground detection device is characterized in that, this device comprises:
Difference point acquiring unit is used for current input image and background image are compared, and obtains the difference point corresponding with each pixel difference of current input image;
The number percent acquiring unit is used for all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtains respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, and n is the positive integer greater than 1;
The threshold selection unit is used in the threshold interval of threshold value i～threshold value j, arbitrarily selected threshold value; Wherein, the value of the number percent that the number percent acquiring unit obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
The prospect judging unit is used for utilizing selected threshold decision to go out the foreground pixel point of current input image;
Wherein, described threshold selection unit comprises:
Curve is set up subelement, is used for number percent value that the value that increases progressively take threshold value 1～threshold value n obtains as horizontal ordinate, number percent acquiring unit as ordinate, sets up the number percent curve;
Slope obtains subelement, be used for obtaining described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
Interval selected subelement is used at described slope curve, and selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The first computation subunit is for the average D of the slope value in the threshold interval that calculates selected threshold value 1～threshold value i-1
AvgAnd variances sigma;
The second computation subunit, the average D that foundation the first computation subunit obtains
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
Relatively choose subelement, be used at described slope curve, from slope value all threshold values less than the second computation subunit acquired results of correspondence, selected value is near of threshold value i.
7. foreground detection device as claimed in claim 6, it is characterized in that, this device further comprises level and smooth decision unit, be used for carrying out walkaway with exist together some frame test patterns of a monitoring scene of described current input image, if the noise level that detects reaches predetermined degree, then first described current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to described difference point acquiring unit; Otherwise, directly described current input image is exported to described difference point acquiring unit.
8. foreground detection device as claimed in claim 7 is characterized in that, described level and smooth decision unit comprises:
Difference statistics subelement is used for each frame and the background image of some frame test patterns are compared, and obtains the difference point corresponding with each pixel difference of each frame test pattern;
The quantity statistics subelement, be used for each difference point of each frame test pattern is compared with a upper limit threshold and a lower limit threshold value respectively, and statistics obtains in each difference point of each frame test pattern, and value is respectively greater than the difference point quantity of upper limit threshold and lower threshold;
The enforcement of the judgment subelement, be used for to calculate each frame test pattern greater than the difference point quantity of lower threshold and poor greater than the difference point quantity of upper limit threshold, if the mean value of the described difference that each frame test pattern is corresponding reaches the predetermined value that an expression noise level reaches predetermined extent, then first described current input image is carried out picture smooth treatment, and then export the current input image after the picture smooth treatment to described difference point acquiring unit; Otherwise, directly described current input image is exported to described difference point acquiring unit.
9. foreground detection device as claimed in claim 6, it is characterized in that, this device is between described threshold selection unit and described prospect judging unit, further comprise the threshold smoothing unit, be used for being utilized as former frame or the selected threshold value of multiframe, the threshold value that the threshold selection unit is selected is carried out smoothing processing;
And described prospect judging unit utilizes the threshold decision after the smoothing processing to go out foreground pixel point in the current input image.
10. foreground detection device as claimed in claim 9 is characterized in that, described threshold smoothing unit comprises:
The threshold value storing sub-units is used for being stored as the selected threshold value of former frame;
The level and smooth subelement of carrying out is used for according to following formula, is utilized as the selected threshold value of former frame the selected threshold value in threshold selection unit is carried out smoothing processing:
T
k’=（1-β）T
k-1+βT
k；
Wherein, T
k' be the threshold value after the smoothing processing, weight, the T that β is Set arbitrarily
K-1To be the selected threshold value of former frame, T
kFor the threshold selection unit is that the selected threshold value of current input image, k are the positive integer greater than 1.
11. an adaptive threshold control method is characterized in that, the method comprises:
A1, current input image and background image are compared, obtain and each pixel of current input image corresponding difference point respectively;
A2, all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtain respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, n is the positive integer greater than 1;
The threshold interval of a3, selected threshold value i～threshold value j, and in this selected threshold interval, select arbitrarily a threshold value; Wherein, the value of the number percent that step a2 obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
Wherein, described step a3 comprises:
The number percent value that a31, the value that increases progressively take threshold value 1～threshold value n obtain as horizontal ordinate, step a2 is set up the number percent curve as ordinate;
A32, obtain described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
A33, in described slope curve, selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The average D of the slope value in the threshold interval of threshold value 1～threshold value i-1 that a34, calculating are selected
AvgAnd variances sigma;
A35, according to the resulting average D of step a34
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
A36, in described slope curve, from slope value all threshold values less than step a35 acquired results of correspondence, selected value is near of threshold value i.
12. adaptive threshold control method as claimed in claim 11 is characterized in that, after the described step a3, the method further comprises: a3 ', be utilized as former frame or the selected threshold value of multiframe, the threshold value that step a3 is selected is carried out smoothing processing.
13. adaptive threshold control method as claimed in claim 12 is characterized in that, described step a3 ' is utilized as the selected threshold value of former frame the selected threshold value of step a3 is carried out smoothing processing according to following formula:
T
k’=（1-β）T
k-1+βT
k；
Wherein, T
k' be the threshold value after the smoothing processing, weight, the T that β is Set arbitrarily
K-1To be the selected threshold value of former frame, T
kFor the selected threshold value of step a3, k are the positive integer greater than 1.
14. an adaptive thresholding value adjusting device is characterized in that, this device comprises:
Difference point acquiring unit is used for current input image and background image are compared, and obtains the difference point corresponding with each pixel difference of current input image;
The number percent acquiring unit is used for all difference points are compared with threshold value 1～threshold value n that value increases progressively successively, obtains respectively value greater than the number percent of the difference point quantity of each among threshold value 1～threshold value n, and n is the positive integer greater than 1;
The threshold selection unit is used for the threshold interval of selected threshold value i～threshold value j, and selectes arbitrarily a threshold value in this selected threshold interval; Wherein, the value of the number percent that the number percent acquiring unit obtains in the threshold interval of threshold value 1～threshold value i-1 bust and in the threshold interval of threshold value i～threshold value j bust slow down, i is more than or equal to 1 and less than n, j is greater than i and less than or equal to n;
Wherein, described threshold selection unit comprises:
Curve is set up subelement, is used for number percent value that the value that increases progressively take threshold value 1～threshold value n obtains as horizontal ordinate, number percent acquiring unit as ordinate, sets up the number percent curve;
Slope obtains subelement, be used for obtaining described number percent slope of a curve, and the value that still increases progressively take threshold value 1～threshold value n makes up slope curve as horizontal ordinate;
Interval selected subelement is used at described slope curve, and selected and value level off to the threshold interval of 0 the corresponding threshold value i of slope～threshold value j, and slope value corresponding to the threshold interval of threshold value 1～threshold value i-1 is far longer than 0;
The first computation subunit is for the average D of the slope value in the threshold interval that calculates selected threshold value 1～threshold value i-1
AvgAnd variances sigma;
The second computation subunit, the average D that foundation the first computation subunit obtains
AvgAnd variances sigma, calculate
The result,
Constant for expression Gaussian distribution interval;
Relatively choose subelement, be used at described slope curve, from slope value all threshold values less than the second computation subunit acquired results of correspondence, selected value is near of threshold value i.
15. adaptive thresholding value adjusting device as claimed in claim 14 is characterized in that, this device further comprises:
The threshold smoothing unit is used for being utilized as former frame or the selected threshold value of multiframe, and the threshold value that the threshold selection unit is selected is carried out smoothing processing.
16. adaptive thresholding value adjusting device as claimed in claim 15 is characterized in that, described threshold smoothing unit comprises:
The threshold value storing sub-units is used for being stored as the selected threshold value of former frame;
The level and smooth subelement of carrying out is used for according to following formula, is utilized as the selected threshold value of former frame the selected threshold value in threshold selection unit is carried out smoothing processing:
T
k’=（1-β）T
k-1+βT
k；
Wherein, T
k' be the threshold value after the smoothing processing, weight, the T that β is Set arbitrarily
K-1To be the selected threshold value of former frame, T
kFor the threshold selection unit is that the selected threshold value of current input image, k are the positive integer greater than 1.
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