CN108921834A - A kind of human body foreground detection system - Google Patents
A kind of human body foreground detection system Download PDFInfo
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- CN108921834A CN108921834A CN201810676902.5A CN201810676902A CN108921834A CN 108921834 A CN108921834 A CN 108921834A CN 201810676902 A CN201810676902 A CN 201810676902A CN 108921834 A CN108921834 A CN 108921834A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G—PHYSICS
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract
The present invention provides a kind of human body foreground detection systems, including image collection module, image pre-processing module, first foreground detection module, image parameter determining module, optical sieving module and the second foreground detection module, described image obtains module for obtaining detection image, described image preprocessing module is for being filtered detection image, the first foreground detection module is for detecting the human body prospect of detection image, described image parameter determination module is used to determine the characteristic parameter of image, described image screening module is used to reject underproof detection image according to the characteristic parameter of image, the second foreground detection module is used to detect human body prospect according to the detection image after the unqualified detection image of rejecting.Beneficial effects of the present invention are：The accurate acquisition and human body foreground detection for realizing image, overcome previous foreground detection and carry out foreground detection using the image that may destroy background model in the process, improve the robustness of foreground detection.
Description
Technical field
The present invention relates to Human Detection fields, and in particular to a kind of human body foreground detection system.
Background technique
With the development of society, service robot progresses into daily life.Currently, service robot often only
It can be researched and developed by professional technician for specific application field, and autonomous fit has not been reached yet in the level of intelligence of robot
The degree of different occasions is answered, therefore affects the universal of robot.Human-computer interaction is the key technology as service robot, right
Service-delivery machine man-based development plays important impetus.And carrying out accurate detection to human body is that service robot is urgently to be resolved
Problem.
Summary of the invention
In view of the above-mentioned problems, the present invention is intended to provide a kind of human body foreground detection system.
The purpose of the present invention is realized using following technical scheme：
Provide a kind of human body foreground detection system, including the inspection of image collection module, image pre-processing module, the first prospect
Module, image parameter determining module, optical sieving module and the second foreground detection module are surveyed, described image obtains module for obtaining
Detection image is taken, described image preprocessing module is for being filtered detection image, the first foreground detection module
It being detected for the human body prospect to detection image, described image parameter determination module is used to determine the characteristic parameter of image,
Described image screening module is used to reject underproof detection image, the second foreground detection mould according to the characteristic parameter of image
Block is used to detect human body prospect according to the detection image after the unqualified detection image of rejecting.
Beneficial effects of the present invention are：The accurate acquisition and human body foreground detection for realizing image, by being carried out to image
Screening overcomes previous foreground detection and carries out foreground detection using the image that may destroy background model in the process, before improving
The robustness of scape detection.
Optionally, described image parameter determination module includes fisrt feature parameter determination module, the determination of second feature parameter
Module and third feature parameter determination module, the fisrt feature parameter determination module are used to determine the fisrt feature ginseng of image
Number, the second feature parameter determination module are used to determine that the second feature parameter of image, the third feature parameter to determine mould
Block is used to determine the third feature parameter of image.
Optionally, the fisrt feature parameter determination module is used to determine the fisrt feature parameter of image, specially：
By image in HSL space representation, indicate that background image, P (t) indicate that the image of t moment, P (t-1) indicate with P (0)
The image at t-1 moment；
Image fisrt feature parameter C is determined using following formula1(t)：
In formula, C1(t) the fisrt feature parameter of image is indicated, M indicates that the width of image, N indicate that the height of image, x and y indicate
The xth row and y of image arrange, and l (t, x, y) indicates the brightness value of P (t) at position (x, y), T1Indicate preset brightness
Threshold value；The fisrt feature parameter is bigger, indicates that t moment brightness of image is higher.
Optionally, the second feature parameter determination module is used to determine the second feature parameter of image, specially：
Image is indicated in gray space, subtracts each other to obtain gray scale difference component with original image and filtered gray level image
As Pa, binary edge map P is converted for grey scale difference image using following formulab：
In formula, Pb(x, y) indicates binary edge map PbIn the pixel value of position (x, y), Pa(x, y) indicates grey scale difference image Pa?
The pixel value of position (x, y), T2Indicate preset binarization threshold；
If the edge image of P (0) is Pb(0), the edge image of P (t) is Pb(t), the edge image of P (t-1) is Pb(t-
1) image second feature parameter C, is determined using following formula2(t, 0), C2(t, t-1)：
In formula, C2(t, 0), C2(t, t-1) indicates the second feature parameter of image,Indicate xor operation, ∪ is indicated or behaviour
Make,S[Pb(t)∪Pb(t-1)] difference table
Show[Pb(t)∪Pb(t-1)] intermediate value is not equal to 0 picture
The number of element；The second feature parameter is bigger, indicates that the difference degree between two images edge is bigger.
Optionally, the third feature parameter determination module is used to determine the third feature parameter of image, specially：
Image third feature parameter C is determined using following formula3(t)：
In formula, C3(t) the third feature parameter of image, P are indicatedf(t) t moment bianry image is indicated, the value of foreground pixel is
255, the value of background pixel is 0, S (Pf(t)) P is indicatedf(t) number of pixel of the intermediate value not equal to 0；The third feature parameter
Bigger, the ratio that the number of expression t moment foreground pixel accounts for total number of image pixels is higher.
Optionally, described image screening module includes the first optical sieving module, the second optical sieving module and third figure
As screening module, the first image screening module for rejecting to similar image, use by the second optical sieving module
It is rejected in the violent image of scene changes, the third optical sieving module is used for the image that wrong foreground detection occurs
It is rejected.
Optionally, the first image screening module is for rejecting similar image, specially：If meeting C simultaneously2
(t, 0) < Y0, C2(t, t-1) < Y0, C3(t-1) < Y1, then rejected using the image as similar image, wherein Y0It indicates
The whether identical threshold value in two images edge is judged, if being less than the threshold value, then it is assumed that two images are identical, Y1Indicate judgement prospect
The whether negligible threshold value of object, if being less than the threshold value, then it is assumed that prospect can be ignored；
The second optical sieving module is for rejecting the violent image of scene changes, specially：If meeting C1(t)
> Y2, then it is assumed that image scene variation acutely, is rejected, wherein Y2Indicate judge image whether be high-brghtness picture images threshold
Value, if more than the threshold value, then image is high-brghtness picture images；
The third optical sieving module is used to reject the image that wrong foreground detection occurs, specially：If full
Sufficient C2(t, 0) > Y0, C2(t, t-1) > Y0, then rejected the image as the image that wrong foreground detection occurs.
Detailed description of the invention
The present invention will be further described with reference to the accompanying drawings, but the embodiment in attached drawing is not constituted to any limit of the invention
System, for those of ordinary skill in the art, without creative efforts, can also obtain according to the following drawings
Other attached drawings.
Fig. 1 is structural schematic diagram of the invention；
Appended drawing reference：
Image collection module 1, image pre-processing module 2, the first foreground detection module 3, image parameter determining module 4, figure
As screening module 5, the second foreground detection module 6.
Specific embodiment
The invention will be further described with the following Examples.
Referring to Fig. 1, a kind of human body foreground detection system of the present embodiment, including image collection module 1, image preprocessing mould
Block 2, the first foreground detection module 3, image parameter determining module 4, optical sieving module 5 and the second foreground detection module 6, it is described
Image collection module 1 is used to be filtered detection image for obtaining detection image, described image preprocessing module 2,
The first foreground detection module 3 for detecting to the human body prospect of detection image, use by described image parameter determination module 4
In the characteristic parameter for determining image, described image screening module 5 is used to reject underproof detection according to the characteristic parameter of image
Image, the second foreground detection module 6 be used for according to reject the detection image after unqualified detection image to human body prospect into
Row detection.
The present embodiment realizes the accurate acquisition of image and human body foreground detection is overcome by screening to image
Foreground detection is carried out using the image that may destroy background model during previous foreground detection, improves the robust of foreground detection
Property.
Preferably, described image parameter determination module 4 is determined including fisrt feature parameter determination module, second feature parameter
Module and third feature parameter determination module, the fisrt feature parameter determination module are used to determine the fisrt feature ginseng of image
Number, the second feature parameter determination module are used to determine that the second feature parameter of image, the third feature parameter to determine mould
Block is used to determine the third feature parameter of image；
The fisrt feature parameter determination module is used to determine the fisrt feature parameter of image, specially：
By image in HSL space representation, indicate that background image, P (t) indicate that the image of t moment, P (t-1) indicate with P (0)
The image at t-1 moment；
Image fisrt feature parameter C is determined using following formula1(t)：
In formula, C1(t) the fisrt feature parameter of image is indicated, M indicates that the width of image, N indicate that the height of image, x and y indicate
The xth row and y of image arrange, and l (t, x, y) indicates the brightness value of P (t) at position (x, y), T1Indicate preset brightness
Threshold value；The fisrt feature parameter is bigger, indicates that t moment brightness of image is higher；
The second feature parameter determination module is used to determine the second feature parameter of image, specially：
Image is indicated in gray space, subtracts each other to obtain gray scale difference component with original image and filtered gray level image
As Pa, binary edge map P is converted for grey scale difference image using following formulab：
In formula, Pb(x, y) indicates binary edge map PbIn the pixel value of position (x, y), Pa(x, y) indicates grey scale difference image Pa?
The pixel value of position (x, y), T2Indicate preset binarization threshold；
If the edge image of P (0) is Pb(0), the edge image of P (t) is Pb(t), the edge image of P (t-1) is Pb(t-
1) image second feature parameter C, is determined using following formula2(t, 0), C2(t, t-1)：
In formula, C2(t, 0), C2(t, t-1) indicates the second feature parameter of image,Indicate xor operation, ∪ is indicated or behaviour
Make,S[Pb(t)∪Pb(t-1)] difference table
Show[Pb(t)∪Pb(t-1)] intermediate value is not equal to 0 picture
The number of element；The second feature parameter is bigger, indicates that the difference degree between two images edge is bigger；
The third feature parameter determination module is used to determine the third feature parameter of image, specially：
Image third feature parameter C is determined using following formula3(t)：
In formula, C3(t) the third feature parameter of image, P are indicatedf(t) t moment bianry image is indicated, the value of foreground pixel is
255, the value of background pixel is 0, S (Pf(t)) P is indicatedf(t) number of pixel of the intermediate value not equal to 0；The third feature parameter
Bigger, the ratio that the number of expression t moment foreground pixel accounts for total number of image pixels is higher；
This preferred embodiment is changed using the variation of brightness of image and edge feature as image parameter by characteristics of image
Parameter describes input picture and foreground detection result.By describing input picture, avoid using background model may be destroyed
The update of image progress background model；Specifically, fisrt feature parameter is bigger, indicate that t moment brightness of image is higher, second feature
Parameter is bigger, indicates that the difference degree between two images edge is bigger, and third feature parameter is bigger, indicates t moment prospect picture
The ratio that the number of element accounts for total number of image pixels is higher；
Preferably, described image screening module 5 includes the first optical sieving module, the second optical sieving module and third figure
As screening module, the first image screening module for rejecting to similar image, use by the second optical sieving module
It is rejected in the violent image of scene changes, the third optical sieving module is used for the image that wrong foreground detection occurs
It is rejected；
The first image screening module is for rejecting similar image, specially：If meeting C simultaneously2(t, 0) <
Y0, C2(t, t-1) < Y0, C3(t-1) < Y1, then rejected using the image as similar image, wherein Y0Expression judges two width
The whether identical threshold value in image border, if being less than the threshold value, then it is assumed that two images are identical, Y1Indicate whether judge foreground object
Negligible threshold value, if being less than the threshold value, then it is assumed that prospect can be ignored；
The second optical sieving module is for rejecting the violent image of scene changes, specially：If meeting C1(t)
> Y2, then it is assumed that image scene variation acutely, is rejected, wherein Y2Indicate judge image whether be high-brghtness picture images threshold
Value, if more than the threshold value, then image is high-brghtness picture images；
The third optical sieving module is used to reject the image that wrong foreground detection occurs, specially：If full
Sufficient C2(t, 0) > Y0, C2(t, t-1) > Y0, then rejected the image as the image that wrong foreground detection occurs；
This preferred embodiment realizes filtering out for image, ensure that the accuracy of display foreground detection, specifically, according to the
One characteristic parameter, second feature parameter and third feature image are to similar image, scene acute variation image and before occurring mistake
The image of scape detection is rejected, and the accurate rejecting of image is realized.
Through the above description of the embodiments, those skilled in the art can be understood that it should be appreciated that can
To realize the embodiments described herein with hardware, software, firmware, middleware, code or its any appropriate combination.For hardware
It realizes, processor can be realized in one or more the following units：Specific integrated circuit (ASIC), digital signal processor
(DSP), digital signal processing appts (DSPD), programmable logic device (PLD), field programmable gate array (FPGA), processing
Device, controller, microcontroller, microprocessor, other electronic units designed for realizing functions described herein or combinations thereof.
For software implementations, some or all of embodiment process can instruct relevant hardware to complete by computer program.
When realization, above procedure can be stored in computer-readable medium or as the one or more on computer-readable medium
Instruction or code are transmitted.Computer-readable medium includes computer storage media and communication media, wherein communication media packet
It includes convenient for from a place to any medium of another place transmission computer program.Storage medium can be computer can
Any usable medium of access.Computer-readable medium can include but is not limited to RAM, ROM, EEPROM, CD-ROM or other
Optical disc storage, magnetic disk storage medium or other magnetic storage apparatus or can be used in carry or store have instruction or data
The desired program code of structure type simultaneously can be by any other medium of computer access.
Finally it should be noted that the above embodiments are merely illustrative of the technical solutions of the present invention, rather than the present invention is protected
The limitation for protecting range, although being explained in detail referring to preferred embodiment to the present invention, the ordinary skill destination of this field
It should be appreciated that can be with modification or equivalent replacement of the technical solution of the present invention are made, without departing from technical solution of the present invention
Spirit and scope.
Claims (7)
1. a kind of human body foreground detection system, which is characterized in that before image collection module, image pre-processing module, first
Scape detection module, image parameter determining module, optical sieving module and the second foreground detection module, described image obtain module and use
In obtaining detection image, described image preprocessing module is for being filtered detection image, first foreground detection
For module for detecting to the human body prospect of detection image, described image parameter determination module is used to determine the feature ginseng of image
Number, described image screening module are used to reject underproof detection image, the second prospect inspection according to the characteristic parameter of image
Module is surveyed to be used to detect human body prospect according to the detection image after the unqualified detection image of rejecting.
2. human body foreground detection system according to claim 1, which is characterized in that described image parameter determination module includes
Fisrt feature parameter determination module, second feature parameter determination module and third feature parameter determination module, the fisrt feature
Parameter determination module is used to determine the fisrt feature parameter of image, and the second feature parameter determination module is for determining image
Second feature parameter, the third feature parameter determination module are used to determine the third feature parameter of image.
3. human body foreground detection system according to claim 2, which is characterized in that the fisrt feature parameter determination module
For determining the fisrt feature parameter of image, specially：
By image in HSL space representation, indicate that background image, P (t) indicate that the image of t moment, P (t-1) indicate t-1 with P (0)
The image at moment；
Image fisrt feature parameter C is determined using following formula1(t)：
In formula, C1(t) the fisrt feature parameter of image is indicated, M indicates that the width of image, N indicate that the height of image, x and y indicate image
Xth row and y column, l (t, x, y) indicate P (t) brightness value at position (x, y), T1Indicate preset luminance threshold
Value；The fisrt feature parameter is bigger, indicates that t moment brightness of image is higher.
4. human body foreground detection system according to claim 3, which is characterized in that the second feature parameter determination module
For determining the second feature parameter of image, specially：
Image is indicated in gray space, is subtracted each other to obtain grey scale difference image P with original image and filtered gray level imagea,
Binary edge map P is converted for grey scale difference image using following formulab：In formula,
Pb(x, y) indicates binary edge map PbIn the pixel value of position (x, y), Pa(x, y) indicates grey scale difference image PaIn position
The pixel value of (x, y), T2Indicate preset binarization threshold；
If the edge image of P (0) is Pb(0), the edge image of P (t) is Pb(t), the edge image of P (t-1) is Pb(t-1), it adopts
Image second feature parameter C is determined with following formula2(t, 0), C2(t, t-1)：
In formula, C2(t, 0), C2(t, t-1) indicates the second feature parameter of image,Indicating xor operation, ∪ is indicated or operation,S[Pb(t)∪Pb(0)]、S[Pb(t)∪Pb(t-1)] it respectively indicates[Pb(t)∪Pb(0)]、[Pb(t)∪Pb(t-1)] intermediate value is not equal to 0 pixel
Number；The second feature parameter is bigger, indicates that the difference degree between two images edge is bigger.
5. human body foreground detection system according to claim 4, which is characterized in that the third feature parameter determination module
For determining the third feature parameter of image, specially：
Image third feature parameter C is determined using following formula3(t)：
In formula, C3(t) the third feature parameter of image, P are indicatedf(t) t moment bianry image is indicated, the value of foreground pixel is 255,
The value of background pixel is 0, S (Pf(t)) P is indicatedf(t) number of pixel of the intermediate value not equal to 0；The third feature parameter is bigger,
The ratio that the number of expression t moment foreground pixel accounts for total number of image pixels is higher.
6. human body foreground detection system according to claim 5, which is characterized in that described image screening module includes first
Optical sieving module, the second optical sieving module and third optical sieving module, the first image screening module are used for phase
It is rejected like image, the second optical sieving module is for rejecting the violent image of scene changes, the third figure
As screening module is used to reject the image that wrong foreground detection occurs.
7. human body foreground detection system according to claim 6, which is characterized in that the first image screening module is used for
Similar image is rejected, specially：If meeting C simultaneously2(t, 0) < Y0, C2(t, t-1) < Y0, C3(t-1) < Y1, then will
The image is rejected as similar image, wherein Y0Expression judges the whether identical threshold value in two images edge, should if being less than
Threshold value, then it is assumed that two images are identical, Y1Expression judges the whether negligible threshold value of foreground object, if being less than the threshold value,
Think that prospect can be ignored；
The second optical sieving module is for rejecting the violent image of scene changes, specially：If meeting C1(t) > Y2,
Then think image scene variation acutely, is rejected, wherein Y2Indicate judge image whether be high-brghtness picture images threshold value,
If more than the threshold value, then image is high-brghtness picture images；
The third optical sieving module is used to reject the image that wrong foreground detection occurs, specially：If meeting C2
(t, 0) > Y0, C2(t, t-1) > Y0, then rejected the image as the image that wrong foreground detection occurs.
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