CN108255288A - Gesture detecting method based on acceleration compensation and complexion model - Google Patents
Gesture detecting method based on acceleration compensation and complexion model Download PDFInfo
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- CN108255288A CN108255288A CN201611240826.0A CN201611240826A CN108255288A CN 108255288 A CN108255288 A CN 108255288A CN 201611240826 A CN201611240826 A CN 201611240826A CN 108255288 A CN108255288 A CN 108255288A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/017—Gesture based interaction, e.g. based on a set of recognized hand gestures
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/10—Human or animal bodies, e.g. vehicle occupants or pedestrians; Body parts, e.g. hands
- G06V40/107—Static hand or arm
- G06V40/113—Recognition of static hand signs
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Abstract
The present invention proposes a kind of gesture detecting method based on acceleration compensation and complexion model, includes the following steps：S1 image preprocessings pre-process the images of gestures of camera capture；S2 acceleration and angle calculation calculate the acceleration and angle change of pixel in image；S3 illumination balances, and illumination variation is compensated using histogram equalization；S4 gestures detections are detected human hand parts of images using complexion model；S5 image filterings filter non-gesture part, extract images of gestures.The present invention program carries out eliminating head jitter or the mobile interference for extracting human hand coordinate using acceleration transducer acquisition acceleration information and calculating inclination angle, complexion model is fitted using linear equation, gestures detection is realized using skin color segmentation, reduce the interference brought due to body shake, improve the accuracy of gestures detection.
Description
Technical field
The present invention relates to field of human-computer interaction, and in particular to a kind of gestures detection based on acceleration compensation and complexion model
Method.
Background technology
In order to help disabled person/the elderly keep with the exchanging of the external world, link up, improve their independent living ability, subtract
Light family, the burden of society, all over the world many scientists start the novel man-machine interaction mode of exploratory development.So-called interaction
Technology includes people and the interaction of executing agency (such as robot) and the interaction of executing agency and environment.The former meaning is can
It is gone to realize the planning and decision that executing agency is difficult in unknown or uncertain condition by people；And be can for the meaning of the latter
By robot go to complete people job task in inaccessiable adverse circumstances or long distance environment.
Traditional human-computer interaction device mainly has keyboard, mouse, handwriting pad, touch screen, game console etc., these equipment
The function of human-computer interaction is realized using the hand exercise of user.Gesture interaction supports more more natural interactive modes, carries
Human-centred rather than facility center management interaction technique is supplied, being primarily focused on original this thereby using family does
In thing and content rather than concentrate in equipment.
Common gesture interaction technology is divided into the gesture interaction technology based on data glove sensor and is regarded based on computer
Two kinds of the gesture interaction technology of feel.
Gesture interaction technology based on data glove sensor needs user to wear data glove or position sensor etc.
Hardware device acquires the information such as finger state and movement locus using sensor, computer is allowed to identify so as to carry out calculation process
Gesture motion realizes various interactive controllings.This mode advantage is to identify that accurate robust performance is good, algorithm is relatively easy, operation
Data are few and quick, can precisely obtain the solid space action of hand, change without the ambient lighting of vision system and carry on the back completely
The problems such as scape is complicated interferes.Shortcoming is that equipment wearing is complicated, of high cost, user's operation is inconvenient for use and gesture motion is by certain
Restrict, therefore, it is difficult to largely put into actual production to use.
Gesture interaction technology based on computer vision by machine vision to camera collected gesture image sequence
Processing identification, so as to be interacted with computer, this method acquires gesture information using camera, the constraint to human hand interaction
Less, interactive experience is more preferable, recognition accuracy higher, and the accuracy rate of the gesture interaction technology is dependent on the accurate of gestures detection
Property.But containing much information of obtaining of this method, processing data model difficulty is big, and when human hand is shaken, verification and measurement ratio can drop
It is low.
Invention content
The purpose of the present invention is to overcome the deficiency in the prior art, especially solves the existing gesture interaction based on computer vision
In technology, acquisition contains much information, and processing data model difficulty is big, and when human hand is shaken, recognition rate can reduce
Problem.
In order to solve the above technical problems, the present invention proposes a kind of gestures detection side based on acceleration compensation and complexion model
Method, key step include：
S1, image preprocessing pre-process the images of gestures of camera capture；
S2, acceleration and angle calculation calculate the acceleration and angle change of pixel in image；
S3, illumination balance, compensates illumination variation using histogram equalization；
S4, gestures detection are detected human hand parts of images using complexion model；
S5, image filtering filter non-gesture part, extract images of gestures.
Further, the step S1 image pretreatment operations include：Image gray processing, image smoothing, image binaryzation
Operation；
Further, in the step S2 acceleration calculations operation, acceleration value variation speed collaboration image gesture is utilized
Caused by coordinate points movement speed determines whether head or body shake, sat using acceleration value angle of inclination collaboration image gesture
Punctuate moving range is determined whether caused by head or body movement；
Further, in the step S4 gestures detections operation, complexion model is fitted using linear equation.
The present invention has following advantageous effect compared with prior art：
The present invention program carries out elimination head jitter using acceleration transducer acquisition acceleration information and calculating inclination angle
Or the mobile interference extracted to human hand coordinate, complexion model is fitted using linear equation, hand is realized using skin color segmentation
Gesture detects, and reduces the interference brought due to body shake, improves the accuracy of gestures detection.
Description of the drawings
Fig. 1 is the flow the present invention is based on acceleration compensation and one embodiment of the gesture detecting method of complexion model
Figure.
Fig. 2 is the acceleration squint angle schematic diagram of the embodiment of the present invention.
Fig. 3 is the design sketch that gestures detection is carried out using complexion model of the embodiment of the present invention.
Fig. 4 is the image filtering design sketch of the embodiment of the present invention.
Specific embodiment
Below in conjunction with the accompanying drawings and specific embodiment the present invention is carried out in further detail with complete explanation.It is appreciated that
It is that specific embodiment described herein is only used for explaining the present invention rather than limitation of the invention.
Referring to Fig. 1, the gesture detecting method based on acceleration compensation and complexion model of the embodiment of the present invention, key step
Including：
S1, image preprocessing, detailed process include：S11 image gray processings, S13 image binaryzations, S12 image smoothings.
S11 image gray processings：Camera obtain eye image be coloured image, comprising contain much information, image procossing speed
Degree is slower.High in view of requirement of the human-computer interaction to real-time, it is necessary that gray processing processing is carried out to coloured image.Gray processing
The process for exactly making R, G of colour element, B component value equal, the gray value in gray level image are equal to the RGB in original color image
Average value, i.e.,
Gray=(R+G+B)/3 (1)
S12 image binaryzations：Image binaryzation is carried out with maximum variance between clusters, process is：
If image shares L gray level, gray value is that the pixel of i shares niA, image shares N number of pixel, normalizing
Change grey level histogram, enable
A threshold value t is set, pixel is divided by c according to gray value0And c1Two classes.c0Probability ω0, mean μ0：
c1Probability ω1, mean μ1：
Wherein,It can thus be appreciated that c0And c1Inter-class variance σ2(t) it is：
σ2(t)=ω0(μ-μ0)2+ω1(μ1-μ)2 (6)
Then t is subjected to value from 0 to i, t is optimal threshold when σ is maximized, you can obtains best binary picture
Picture.
S13 image smoothings：Bianry image is carried out smoothly with Mathematical Morphology Method, reduction noise in image, process is：
1) operator of corrosion is Θ, and set A is aggregated B corrosion and is defined as formula (7)：
2) operator of expansion isSet A is aggregated B expansions and is defined as formula (8)：
Using dilation erosion type gradient operator, i.e., the image after subtracting corrosion with the image after expansion, you can obtain image
In edge.Since edge at this time is not that single pixel wide connects, it is also necessary to again with region framework extraction algorithm to edge into
Row refinement.
3) it is image to set B, and S (A) represents the skeleton of A, and B is structural element, then is represented with formula (9)：
Wherein, K represents to corrode A into the iterations before empty set, i.e., is expressed as with formula (10)：
Sk(A) it is known as skeleton subset, can be written as according to formula (11)：
A Θ kB represent to corrode A with B for continuous k times.
S2, acceleration and angle calculation calculate the acceleration and angle change of pixel in image；It is taken with the typical case of device
To as a reference point, wherein, x-axis and y-axis are in horizontal plane, and z-axis is in and horizontal line.Referring to Fig. 2, Fig. 2 is accelerates
Spend squint angle schematic diagram.Wherein θ, ψ,Respectively acceleration transducer x-axis and horizontal angle, y-axis and horizontal folder
Angle, z-axis and the angle in acceleration of gravity direction.When acceleration transducer is in state shown in Fig. 2 a, x, y, tri- directions of z tilt
Angle is all 0.
Projection of the gravitational vectors in reference axis can be formed equal to acceleration transducer direction and reference axis angle sine value
Output acceleration, x, y, the acceleration output valve in tri- directions of z is Ax, Ay, Az。
Tiltangleθ, ψ,It is respectively calculated as follows：
It can determine whether using acceleration value variation speed collaboration image gesture coordinate points movement speed as head or body
Caused by shake, it can decide whether using acceleration value angle of inclination collaboration image gesture coordinate points moving range as head or body
Caused by body movement.
S3, illumination balance, compensates illumination variation using histogram equalization；Histogram equalization makes the ash of image
Degree spacing is pulled open or makes intensity profile uniform, so as to increase contrast, makes image detail clear, achievees the purpose that image enhancement.Its
Specific method is：
All gray level S of original image are provided firstk(k=0,1 ..., L-1)；Then statistics original image is each
The pixel number n of gray levelk；The accumulation for (14) formula being used to calculate original image again after the histogram of original image is calculated using formula (13)
Histogram：
P(Sk)=nk/ n, k=0,1 ..., L-1 (13)
p(tk)=nk/n (15)
Wherein, n is total number of image pixels.To gray value tkRounding determines Sk→tkMapping relations after count new histogram
Each gray-scale pixel number nk；New histogram is finally calculated using formula (15).
S4, gestures detection are detected human hand parts of images using complexion model；Use determining Cb and Cr maximum values with
And the rectangle colour of skin model of linear equation of minimum value, rectangular model can use four straight lines L1, L2, L3, L4 expressions are as follows：
L1:Cb×T1+T2＜ Cr (16)
L2:Cb×T2+T3＜ Cr (17)
L3:Cb×T5+T6＞ Cr (18)
L4:Cb×T7+T8＞ Cr (19)
Wherein T1=-1.22265625, T2=267.3330078125, T3=0.875, T4=29.375, T5=-
1.3330078125、T6=316.3330078125, T7=0.064453125, T8=170.612903225.Above-mentioned linear equation
The parameter of parted pattern carries out off-line training to Finite Amplitude image and obtains, and passes through the parameter detecting different application of off-line training
The colour of skin under scene.The result of gestures detection is referring to Fig. 3.
S5, image filtering filter non-gesture part, extract images of gestures.Pass through the human hand got from gestures detection
8*8 pixel is extracted in region as colour of skin reference zone, it is straight to four boundaries in linear equation parted pattern using it
Line L1, L2, L3, L4 carry out its intercept adjustment respectively, and the intercept adjusting range of every line is 16 units.In the present embodiment,
The initial intercept of L1 is 267, the intercept of L1 rounding successively in [259,275] during the adjustment, until being adjusted to most preferably cut
Away from until, the intercept of L2, L3, L4 are also to be adjusted according to same method.The judging basis of best intercept is its performance scoring
Value S gets highest one group of intercept.The calculation formula of score value S is as follows
Wherein, NrefTo belong to the number of pixels of the colour of skin, N in colour of skin reference zoneneighTo belong in tracking initiation frame neighborhood
The number of pixels of the colour of skin, ArefFor reference zone size, AneighFor neighborhood size.S is defined as colour of skin reference zone quilt
Detection colour of skin ratio and nontarget area are detected the ratio of colour of skin ratio.The result of image filtering is referring to Fig. 4.
Claims (4)
1. a kind of gesture detecting method based on acceleration compensation and complexion model, which is characterized in that include the following steps：
S1 image preprocessings pre-process the images of gestures of camera capture；
S2 acceleration and angle calculation calculate the acceleration and angle change of pixel in image；
S3 illumination balances, and illumination variation is compensated using histogram equalization；
S4 gestures detections are detected human hand parts of images using complexion model；
S5 image filterings filter non-gesture part, extract images of gestures.
2. a kind of gesture detecting method based on acceleration compensation and complexion model according to claim 1, feature exist
In the step S1 image pretreatment operations include：Image gray processing, image smoothing, image binaryzation operation.
3. a kind of gesture detecting method based on acceleration compensation and complexion model according to claim 1, the step
In the operation of S2 acceleration calculations, head is determined whether using acceleration value variation speed collaboration image gesture coordinate points movement speed
Caused by portion or body shake, head is determined whether using acceleration value angle of inclination collaboration image gesture coordinate points moving range
Or caused by body movement.
4. a kind of gesture detecting method based on acceleration compensation and complexion model according to claim 1, feature exist
In in step S4 gestures detections operation, being fitted using linear equation to complexion model.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109801320A (en) * | 2019-01-26 | 2019-05-24 | 武汉嫦娥医学抗衰机器人股份有限公司 | A kind of dry skin state Intelligent Identify method and system based on facial subregion |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109801320A (en) * | 2019-01-26 | 2019-05-24 | 武汉嫦娥医学抗衰机器人股份有限公司 | A kind of dry skin state Intelligent Identify method and system based on facial subregion |
| CN109801320B (en) * | 2019-01-26 | 2020-12-01 | 武汉嫦娥医学抗衰机器人股份有限公司 | Intelligent skin dryness state identification method and system based on facial partition |
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Application publication date: 20180706 |