CN110222608A - A kind of self-service examination machine eyesight detection intelligent processing method - Google Patents
A kind of self-service examination machine eyesight detection intelligent processing method Download PDFInfo
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- CN110222608A CN110222608A CN201910442135.6A CN201910442135A CN110222608A CN 110222608 A CN110222608 A CN 110222608A CN 201910442135 A CN201910442135 A CN 201910442135A CN 110222608 A CN110222608 A CN 110222608A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
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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
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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
- G06V40/16—Human faces, e.g. facial parts, sketches or expressions
- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07F—COIN-FREED OR LIKE APPARATUS
- G07F17/00—Coin-freed apparatus for hiring articles; Coin-freed facilities or services
- G07F17/04—Coin-freed apparatus for hiring articles; Coin-freed facilities or services for anthropometrical measurements, such as weight, height, strength
Abstract
The embodiment of the invention discloses a kind of self-service examination machine eyesights to detect intelligent processing method characterized by comprising obtains monitoring video information in physical examination cabin；Identify physical examination number in physical examination cabin；Determine the lens wear situation of the examinee；Determine the eye circumstance of occlusion of the examinee；Eyesight detection is carried out to the examinee.The embodiment of the present invention provides a kind of method using artificial intelligence, it is automatically performed number detection in cabin, identification of whether wearing glasses, hides the whether correct sequence of operations of eye mode, the degree of automation for improving eyesight detection, saves manpower, while improving the accuracy of detection.
Description
Technical field
The present embodiments relate to traffic control self-service examination technical fields, and in particular to a kind of self-service examination machine eyesight detection intelligence
Processing method can be changed.
Background technique
It is needed when of the right age personnel requisition's driver's license according to " motor vehicle driving license claims and using regulation (2016 editions) " at present
The physical examination mostly uses manual type to carry out, and small part area is begun trying using self-service examination equipment, into unmanned physical examination.
But self-service examination equipment on the market, intelligent level are not able to satisfy the demand of realistic case, can not intelligent decision it is tested
Whether have more people same cabin, whether wear glasses, whether according to normal mode check situations such as, when occurring in physical examination if surveying
When such as midway substitution, other people intervening acts, it can not also stop physical examination in due course or prevent the generation of such cheating.Need work
Personnel carried out in physical examination real time monitoring check or after uniformly review video and capture pictures judge, this mode
Efficiency is not only influenced, testing result also suffers from the influence such as angle, time, auditor's subjective factor of monitoring, physical examination
The accuracy of validity and result can have a greatly reduced quality greatly.
Summary of the invention
For this purpose, the embodiment of the present invention provides a kind of self-service examination machine eyesight detection intelligent processing method, it is existing to solve
Due to when carrying out eyesight detection in technology, detection environment is lack of standardization, there are knot is detected caused by subjective factor for artificial judgement
The not high problem of fruit accuracy.
To achieve the goals above, the embodiment of the present invention provides a kind of method using artificial intelligence, is automatically performed in cabin
Number detection, hides the sequence of operations such as whether eye mode correct at identification of whether wearing glasses, and improves the automation of eyesight detection
Degree saves manpower.Specific technical solution is as follows:
A kind of self-service examination machine eyesight detection intelligent processing method is provided according to embodiments of the present invention, which is characterized in that
Include:
Obtain monitoring video information in physical examination cabin；
Identify physical examination number in physical examination cabin；
Determine the lens wear situation of the examinee；
Determine the eye circumstance of occlusion of the examinee；
Eyesight detection is carried out to the examinee.
Further, identify that physical examination number includes: in physical examination cabin
According to default video frame speed, the knowledge of human body key position is carried out to the video information based on human body attitude identification model
Not, human body attitude data are obtained；Wherein, the human body attitude data include face's key position attitude data and body key portion
Position attitude data；The key position attitude data includes the coordinate information and score information of key position；
The human body attitude data are analyzed, judge physical examination number in physical examination cabin, complete the identification of physical examination number.
Further, it is determined that the lens wear situation of the examinee includes:
Obtain the human face image information of examinee in the monitoring video information in physical examination cabin；
The human face image information is input to eyeglasses-wearing disaggregated model trained in advance, whether predicts the examinee
It wears glasses, determines the lens wear situation of the examinee.
Further, if identify that physical examination number is single people in physical examination cabin, determine that the eye of the examinee blocks
Situation includes:
Determine the eye locations being blocked；
Human face image information in the monitoring video information is input to cover plate position model trained in advance, prediction
The position of eye-shading plate；
Ratio between the position of the eye locations and the cover plate that are blocked described in calculating；
If the ratio is greater than preset threshold, the eye of the examinee is blocked correctly.
Further, using the Faster R-CNN training eyeglasses-wearing disaggregated model.
Further, it is determined that the eye locations being blocked include:
Face's key position attitude data is fitted using least square method, obtains line function in head；
Using the eye key position filtered out as benchmark position, the eye being blocked is gone out according to the head middle line Function Mapping
Portion's key position determines the eye locations being blocked with this.
Another aspect of the present invention provides a kind of self-service examination machine eyesight detection Intelligent processing device, which is characterized in that
Including obtaining module, identification module, determining eyeglasses-wearing module, determine that eye blocks module, eyesight detection module；
Wherein, the acquisition module is for obtaining monitoring video information in physical examination cabin；
Identification module physical examination number in physical examination cabin for identification；
The determining eyeglasses-wearing module is used to determine the lens wear situation of the examinee；
The determining eye blocks module for determining the eye circumstance of occlusion of the examinee；
The eyesight detection module is used to carry out eyesight detection to the examinee.
Further, the identification module includes human body attitude data computation module and analysis module；Wherein,
The human body attitude data computation module is used for according to default video frame speed, based on human body attitude identification model to institute
It states video information and carries out the identification of human body key position, obtain human body attitude data；
The analysis module judges physical examination number in physical examination cabin, completes examinee for analyzing the human body attitude data
Several identification.
Further, the determining eyeglasses-wearing module includes that human face image information obtains module, model computation module；Its
In,
The human face image information obtains the people that module is used to obtain examinee in the monitoring video information in physical examination cabin
Face image information；
The model computation module is used to for the human face image information being input to eyeglasses-wearing classification mould trained in advance
Type, predicts whether the examinee wears glasses, and determines the lens wear situation of the examinee.
Further, it includes blocking position determining module, model prediction module, radiometer that the determining eye, which blocks module,
Calculate module, judgment module；Wherein,
The blocking position determining module is for determining the eye locations being blocked；
The model prediction module is used to the human face image information in the monitoring video information being input to preparatory training
Cover plate position model, predict the position of eye-shading plate；
The ratio calculation module is for calculating between the eye locations being blocked and the position of the cover plate
Ratio；
For judging that the ratio is greater than preset threshold, the eye of the examinee blocks correctly the judgment module；
The blocking position determining module includes middle line function computation module, maps determining module；
The middle line function computation module is used to carry out face's key position attitude data using least square method
Fitting, obtains line function in head；
The mapping determining module is used for using the eye key position filtered out as benchmark position, according to the head middle line
Function Mapping goes out the eye key position being blocked, and the eye locations being blocked are determined with this.
The embodiment of the present invention has the advantages that
The present invention uses the processing method of artificial intelligence, the automatic identification to physical examination number in physical examination storehouse is completed, to physical examination
The lens wear condition of people, the eye circumstance of occlusion of examinee automatically determine, and are meeting all conditions of preset front
In the case where, eyesight detection is carried out to examinee.Whole process is participated in without artificial, all processes of self-service examination machine automatic processing.
Manpower is saved, while improving the accuracy of detection.
Further, the present invention identifies human body key position using human body attitude identification model, obtains human body appearance
State data, and human body attitude data are repeatedly screened, and then identify the physical examination number in physical examination cabin again.According to human body appearance
State data determine examinee's quantity, can accurately more determine the number in physical examination cabin.It is repeatedly screened before recognition, by body
There is incomplete examinee's all screening and filterings either except fixed area and fall in body, reduce the difficulty of later period recognizer exploitation
Degree, ensure that identification accuracy.
Further, face's key position of examinee is calculated and is analyzed after identification number of the present invention, judge body
It examines whether people is living body people, filters out genuine and believable human body attitude data, prevent the character image on clothes to subsequent number
Judgement has an impact.
Further, after completing recognition of face, if identify that physical examination number is single people in physical examination cabin, every pre-
The period is ordered to examinee's progress face alignment.After being determined above and finishing examinee, prevent that midway from substituting or other people are on doorway
It is prompted, the characteristic value for carrying out once per second to the face of examinee compares, and guarantees in entire physical examination, the one of examinee
Cause property, it is ensured that the authenticity and accuracy of testing result.
Detailed description of the invention
It, below will be to embodiment party in order to illustrate more clearly of embodiments of the present invention or technical solution in the prior art
Formula or attached drawing needed to be used in the description of the prior art are briefly described.It should be evident that the accompanying drawings in the following description is only
It is merely exemplary, it for those of ordinary skill in the art, without creative efforts, can also basis
The attached drawing of offer, which is extended, obtains other implementation attached drawings.
Structure depicted in this specification, ratio, size etc., only to cooperate the revealed content of specification, for
Those skilled in the art understands and reads, and is not intended to limit the invention enforceable qualifications, therefore does not have technical
Essential meaning, the modification of any structure, the change of proportionate relationship or the adjustment of size are not influencing the function of the invention that can be generated
Under effect and the purpose that can reach, should all still it fall in the range of disclosed technology contents obtain and can cover.
Fig. 1 is that a kind of self-service examination machine eyesight that the embodiment of the present invention 1 provides detects intelligent processing method flow diagram；
Fig. 2 is that a kind of self-service examination machine eyesight that the embodiment of the present invention 2 provides detects the excellent of intelligent processing method process
The embodiment flow chart of choosing；
Fig. 3 is examinee's eye key position picture；
Fig. 4 is the height variation diagram of eyes the ratio of width to height；
Fig. 5 is human body attitude data simulation drawing.
Specific embodiment
Embodiments of the present invention are illustrated by particular specific embodiment below, those skilled in the art can be by this explanation
Content disclosed by book is understood other advantages and efficacy of the present invention easily, it is clear that described embodiment is the present invention one
Section Example, instead of all the embodiments.Based on the embodiments of the present invention, those of ordinary skill in the art are not doing
Every other embodiment obtained under the premise of creative work out, shall fall within the protection scope of the present invention.
It is that a kind of self-service examination machine eyesight that the embodiment of the present invention 1 provides detects intelligent processing method process referring to Fig. 1
Block diagram, comprising:
Obtain monitoring video information in physical examination cabin；
Identify physical examination number in physical examination cabin；
Determine the lens wear situation of the examinee；
Determine the eye circumstance of occlusion of the examinee；
Eyesight detection is carried out to the examinee.
The present invention carries out a series of intelligent algorithm calculating to the monitoring video information in physical examination cabin, including to physical examination cabin
The identification of interior physical examination number, the determination to lens wear condition and the determination of the eye circumstance of occlusion to examinee, at this
In the case that a little conditions all meet preset condition, the eyesight of examinee is detected, at the intelligence for realizing eyesight detection
Reason.
The above-mentioned identification to physical examination number in physical examination cabin includes the following steps:
According to default video frame speed, the knowledge of human body key position is carried out to the video information based on human body attitude identification model
Not, human body attitude data are obtained；
The human body attitude data are analyzed, judge physical examination number in physical examination cabin, complete the identification of physical examination number.
For data processing quickly and efficiently property the considerations of, the real-time analysis of 25 frame per second is carried out to the video in cabin,
Above-mentioned human body attitude identification model can be the DensePose of Facebook research institute announcement, be also possible to AlphaPose.It is excellent
Choosing, the present invention is based on the human body attitude identification framework OpenPose of open source, carry out to the human body key position in video information
Identification obtains human body attitude data.OpenPose is the human body attitude identification framework of an open source, can be to the key position of human body
It carries out one to estimate, range is 0 to 1, more credible closer to 1.
The real-time analysis for carrying out 25 frame per second to video in cabin based on OpenPose, to the human body appeared in every frame image
Image carries out the identification of human body key point, due to human body diversity, such as the human body image on examinee's clothes, when hatch door is opened, door
Outer non-examinee's image, so the attitude data calculated OpenPose frame is needed to be filtered, removal interference number
According to.
Above-mentioned human body attitude data include the coordinate information and score information of body key position, and body key position includes
Face's key position and body key position namely the human body attitude data include face's key position attitude data and body
Key position attitude data；The key position attitude data includes the coordinate information and score information of key position；
After human body attitude data are calculated, human body attitude data are screened several times, it is preferred that the present invention is to upper
Human body attitude data are stated to be screened twice.The ratio between human body attitude data and physical examination fixed area is analyzed first, tentatively
Screening does not meet the examinee of preset ratio range.
Specifically, first carrying out ratio screening to human body attitude data.It, can be same due to the shoulder breadth of normal person, the length on head
The selected fixed area in physical examination cabin has a proportional region (proportional region is different according to the difference of selection area specific size),
Human body attitude data are carried out with preliminary screening according to this proportional region.
Secondly, position and proportionate relationship between analysis face's key position attitude data and body key position, to body
It examines people and carries out postsearch screening.Specifically, body key position all meets physiological structure due to normal examinee, for example head exists
On both shoulders, there is also a proportional regions with both shoulders width for the width of head, according to the proportional region of this physiological make-up, to body
It examines people and carries out postsearch screening.
Examinee is carried out after screening twice, then the number of remaining human body attitude data is differentiated, it, can with this
Identify the physical examination number in physical examination cabin.Pass through the screening of the human body attitude data twice to human body attitude data examinee, mistake
It filters the either body for not meeting testing requirements and there is lopsided special population, guarantee that participating in the people of physical examination is all normal body
People is examined, ensure that the accuracy in detection of self-service examination.
The present invention identifies human body key position using human body attitude identification model, obtains human body attitude data, and
Human body attitude data are repeatedly screened, and then identify the physical examination number in physical examination cabin again.It is true according to human body attitude data
Determine examinee's quantity, can accurately more determine the number in physical examination cabin.It is repeatedly screened before recognition, by body, there are residual
It lacks examinee's all screening and filterings either except fixed area to fall, reduces the difficulty of algorithm development, ensure that identification is quasi-
True property.
When identifying that the human body attitude data in physical examination cabin at this time only have a people, to the lens wear condition of the examinee
It is detected.Include the following steps:
Obtain the human face image information of examinee described in the monitoring video information in physical examination cabin；
The human face image information is input to disaggregated model trained in advance, predicts whether the examinee matches hyperphoria with fixed eyeballs
Mirror determines the lens wear situation of the examinee.
First be based on Google Inception V3 disaggregated model, training whether the disaggregated model of wearing spectacles.Specifically,
Using true physical examination cabin backplane environment as background, acquisition is a certain number of to wear glasses and not wearing spectacles, two kinds of positive and negative data sets,
We use 10,000 positive sample pictures, i.e. wearing spectacles image, 10,000 negative sample pictures, i.e., image of not wearing glasses, and 2,000
Open authentication image.Then data are pre-processed, ocular is extracted as Inception V3 disaggregated model
Whether input extracts the calculated characteristic value of Inception V3, is input to the full articulamentum worn glasses He do not worn glasses, to matching
It wears glasses and classifies, obtain the parameters of Inception V3 when nicety of grading reaches the precision of preset requirement, and then obtain
To eyeglasses-wearing disaggregated model.
It should be noted that we train the model come, in continuous use process, recognition correct rate can be mentioned constantly
Height, per a picture is detected in the actual environment, this picture can be added in training pattern library, be continued to optimize out according to actual result
The training pattern of new wearing spectacles, and so on, recognition accuracy solid growth.
Then the human face image information of the examinee in monitoring video information is extracted, the facial image information input is supreme
Eyeglasses-wearing disaggregated model is stated, can be accurately judged to whether examinee wears glasses.If the lens wear condition of examinee with
The pre-selected lens wear condition of examinee is consistent (it should be noted that examinee is entering progress eyesight inspection in physical examination cabin
When survey, can pre-select whether oneself wears glasses on physical-examination machine), then next detection is entered, i.e., to examinee's
Eye circumstance of occlusion is judged.
The eye circumstance of occlusion for determining the examinee includes:
Determine the eye locations being blocked；
By the human face image information in the monitoring video information be input in advance training based on Faster R-CNN's
Cover plate position model predicts the position of eye-shading plate；
Ratio between the position of the eye locations and the cover plate that are blocked described in calculating；
If the ratio is greater than preset threshold, the eye of the examinee is blocked correctly.
When the head end timing of examinee, face's key position of examinee is calculated based on OpenPose, is recycled minimum
Square law is fitted face's key position attitude data, obtains line function in head；With the eye key position filtered out
For benchmark position, the eye key position being blocked is gone out according to head middle line Function Mapping, the eye position being blocked is determined with this
It sets.The calculation formula for calculating least square method residual sum of squares (RSS) is as follows:
Line function in head is determined by Q, obtains β 0, β 1 eventually by derivation, and line function calculation formula is such as in head
Under:
Two parameter betas 0, the β 1 for calculating line function in head, can calculate the mathematic(al) representation of line function in head,
This will not be repeated here.
Based on Faster R-CNN, training eye-shading plate position model predicts the position of eye-shading plate.Specifically, due to hiding eye
Plate resemblance is stablized, that is, Articles detecting model can be used to be detected.A certain number of examinees are obtained first uses screening eye
The picture of plate Articles detecting model can be used to be detected.The figure that a certain number of examinees use eye-shading plate is obtained first
Piece tends to really detect scene, can reduce the false detection rate of eye-shading plate using face as eye-shading plate background.Then to photograph collection into
The boundary marker of row eye-shading plate, since eye-shading plate main region is in coverage region above, only to the upper half of eye-shading plate
Divide and carry out boundary marker, pictures are then sent into Faster R-CNN, train eye-shading plate detection model.
After obtaining eye-shading plate detection model, the human face image information in monitoring video information is input to trained coverage
Board position model predicts the position of eye-shading plate.In order to improve the accuracy of detection, facial image letter to be identified for each frame
Breath is identified three times, exports at most eye-shading plate predicted position three times.
Finally, the predicted position of three eye-shading plates of prediction is subjected to ratio calculation with the eye locations being blocked respectively,
Ratio is denoted as ration, and calculation formula is as follows:One of eye-shading plate is pre- if it exists
When location is set with the ratio of eye eye-shading plate position greater than preset threshold value, the preset threshold value is preferably 0.85, then it is assumed that
It is correct for blocking.
It should be noted that in order to ensure accurately finding out eye blocking position, the present invention is determining the eye locations that are blocked
Before, further comprehensive judgement is carried out based on head pose data and body attitude data of the OpenPose to examinee, sentenced
Whether disconnected examinee is in torticollis posture, if it is be in torticollis posture, then prompt user's head posture inaccuracy or again
The initial conditions of self-service examination are returned to, the identification of physical examination number, the determination of lens wear condition and eye is completed again and blocks feelings
The processes such as the judgement of condition.
It referring to fig. 2, is that a kind of self-service examination machine eyesight that the embodiment of the present invention 2 provides detects intelligent processing method process
Preferred embodiment flow chart.The preferred embodiment includes the following steps:
Obtain monitoring video information in physical examination cabin；
According to default video frame speed, the knowledge of human body key position is carried out to the video information based on human body attitude identification model
Not, human body attitude data are obtained；Wherein, the human body attitude data include face's key position attitude data and body key portion
Position attitude data；The key position attitude data includes the coordinate information and score information of key position；
The human body attitude data are analyzed, judge physical examination number in physical examination cabin, complete the identification of physical examination number；
Face's key position of the examinee is calculated and analyzed, judges whether the examinee is living body people；
Wherein face's key position includes the eyes, nose, ear of examinee；
Determine the lens wear situation of the examinee；
Determine the eye circumstance of occlusion of the examinee；
Face alignment is carried out to the examinee every ticket reserving time section；
Eyesight detection is carried out to the examinee.
In order to optimize the present invention more, on the basis of embodiment 1, to the examinee in physical examination cabin whether be living body into
Row identification.When it is 1 people that human body attitude data after screening and analyzing twice, are identified with the number in physical examination cabin, even
A living body verifying is carried out to video frame in continuous monitoring video information, that is, verifies the artificial true physical examination identified
People.Judge whether examinee is living body by prompting physical examination human body the movements such as to be subjected to displacement, turn one's head and blink.Work as examinee
Body does not occur to be significantly displaced, and turns one's head and blink, then is considered as living body.Its deterministic process is as follows:
It is the picture of examinee's eye key position referring to Fig. 3, eye key position generally has 6 key points, such as Fig. 3
In P1, P2, P3, P4, P5, P6.It carries out 68 key points to face based on OpenPose to calculate, wherein each eyes have 6
Key point, when carrying out blink behavior, the ratio of width to height of eyes can regular height variation, (referring to fig. 4), number of image frames is got over
More, change curve is more smooth.When one second 25 frame, behavior of once blinking, the trend chart of dif is to close one's eyes when minimum point.
The ratio of width to height Dif of eyes can be obtained by following calculation formula:
Dif=(| | p2-p6 | |+| | p3-p5 | |)/2 | | p1-p4 | |
Referring to Fig. 5, it is human body attitude data simulation drawing, can judges whether examinee turns one's head according to the figure.Under normal circumstances,
When normal person is in proper posture, there is certain angle between ear and shoulder, when examinee turns one's head to the right, left ear,
The angle a that right shoulder, left shoulder are constituted can continue to increase, and similarly, when turning one's head to the left, the angle b that auris dextra, left shoulder, right shoulder are constituted also can
It is lasting to increase, determine whether examinee turns one's head by the size variation of detection angles a, angle b, and then whether judge examinee
For living body.
Self-service examination process of the present invention, examinee will complete in physical examination cabin, to prevent midway from substituting or side someone
Prompted on doorway, identifying physical examination number, as soon as and determine when there was only people, no longer use fixed area, but use
The full shot region of shooting carries out human body attitude analysis.When physical examination hatch door is not turned off, doorway position detection to human body attitude number
According to, then it is assumed that improper physical examination occurs, returns to original state when physical examination, completes identification, the eyeglasses-wearing of physical examination number again
The processes such as the determination of situation and the judgement of eye circumstance of occlusion.When physical examination hatch door is closed, detect that human body attitude is a people, then
The aspect ratio pair that once per second is carried out to its face, guarantees the consistency of examinee in entire physical examination.
The process of face alignment is as follows:
First according to the facial image information of the examinee intercepted out, the model for being put into preparatory trained FaceNet comes
128 dimensional feature vector of face is extracted, and is kept records of.The wherein model of FaceNet is that 1w picture is obtained according to collection
It trains.
Secondly according to real-time detection go out face picture, be put into trained FaceNet model obtain 128 dimensional features to
Amount, keeps records of；
Finally calculate the Euclidean distance value of 2 vectors above, rule: if the same person, this value less than 1.05 or so, if
It is the face picture as two, obtained distance can be 0, wherein 1.05 be that we are former by many experiments verifying and Euclidean distance
Reason combines the appropriate threshold obtained.
The embodiment of the present invention 2 is calculated and is analyzed to face's key position of examinee after identification number, judges body
It examines whether people is living body people, filters out genuine and believable human body attitude data, prevent the character image on clothes to subsequent number
Judgement has an impact.
Further, after completing recognition of face, if identify that physical examination number is single people in physical examination cabin, every reservation
Period carries out face alignment to the examinee.After being determined above and finishing examinee, prevent that midway from substituting or other people are in door
Mouth is prompted, and the characteristic value for carrying out once per second to the face of examinee compares, and is guaranteed in entire physical examination, examinee's
Consistency, it is ensured that the authenticity and accuracy of testing result.
Another aspect of the present invention provides a kind of self-service examination machine eyesight detection Intelligent processing device, which is characterized in that
Including obtaining module, identification module, determining eyeglasses-wearing module, determine that eye blocks module, eyesight detection module；
Wherein, the acquisition module is for obtaining monitoring video information in physical examination cabin；
Identification module physical examination number in physical examination cabin for identification；
The determining eyeglasses-wearing module is used to determine the lens wear situation of the examinee；
The determining eye blocks module for determining the eye circumstance of occlusion of the examinee；
The eyesight detection module is used to carry out eyesight detection to the examinee.
Further, the identification module includes human body attitude data computation module and analysis module；Wherein,
The human body attitude data computation module is used for according to default video frame speed, based on human body attitude identification model to institute
It states video information and carries out the identification of human body key position, obtain human body attitude data；
The analysis module judges physical examination number in physical examination cabin, completes examinee for analyzing the human body attitude data
Several identification.
Further, the determining eyeglasses-wearing module includes that human face image information obtains module, model computation module；Its
In,
The human face image information obtains the people that module is used to obtain examinee in the monitoring video information in physical examination cabin
Face image information；
The model computation module is used to for the human face image information being input to eyeglasses-wearing classification mould trained in advance
Type, predicts whether the examinee wears glasses, and determines the lens wear situation of the examinee.
Further, it includes blocking position determining module, model prediction module, radiometer that the determining eye, which blocks module,
Calculate module, judgment module；Wherein,
The blocking position determining module is for determining the eye locations being blocked；
The model prediction module is used to the human face image information in the monitoring video information being input to preparatory training
Cover plate position model, predict the position of eye-shading plate；
The ratio calculation module is for calculating between the eye locations being blocked and the position of the cover plate
Ratio；
For judging that the ratio is greater than preset threshold, the eye of the examinee blocks correctly the judgment module；
The blocking position determining module includes middle line function computation module, maps determining module；
The middle line function computation module is used to carry out face's key position attitude data using least square method
Fitting, obtains line function in head；
The mapping determining module is used for using the eye key position filtered out as benchmark position, according to the head middle line
Function Mapping goes out the eye key position being blocked, and the eye locations being blocked are determined with this.
Although above having used general explanation and specific embodiment, the present invention is described in detail, at this
On the basis of invention, it can be made some modifications or improvements, this will be apparent to those skilled in the art.Therefore,
These modifications or improvements without departing from theon the basis of the spirit of the present invention are fallen within the scope of the claimed invention.
Claims (10)
1. a kind of self-service examination machine eyesight detects intelligent processing method characterized by comprising
Obtain monitoring video information in physical examination cabin；
Identify physical examination number in physical examination cabin；
Determine the lens wear situation of the examinee；
Determine the eye circumstance of occlusion of the examinee；
Eyesight detection is carried out to the examinee.
2. the method according to claim 1, wherein physical examination number includes: in identification physical examination cabin
According to default video frame speed, the identification of human body key position is carried out to the video information based on human body attitude identification model,
Obtain human body attitude data；Wherein, the human body attitude data include face's key position attitude data and body key position
Attitude data；The key position attitude data includes the coordinate information and score information of key position；
The human body attitude data are analyzed, judge physical examination number in physical examination cabin, complete the identification of physical examination number.
3. the method according to claim 1, wherein determining that the lens wear situation of the examinee includes:
Obtain the human face image information of examinee in the monitoring video information in physical examination cabin；
The human face image information is input to eyeglasses-wearing disaggregated model trained in advance, predicts whether the examinee wears
Glasses determine the lens wear situation of the examinee.
4. according to the method described in claim 2, it is characterized in that, if when identifying in physical examination cabin that physical examination number is single people,
The eye circumstance of occlusion for determining the examinee includes:
Determine the eye locations being blocked；
Human face image information in the monitoring video information is input to cover plate position model trained in advance, prediction hides eye
The position of plate；
Ratio between the position of the eye locations and the cover plate that are blocked described in calculating；
If the ratio is greater than preset threshold, the eye of the examinee is blocked correctly.
5. according to the method described in claim 3, it is characterized in that, being classified using the Faster R-CNN training eyeglasses-wearing
Model.
6. according to the method described in claim 4, it is characterized in that, determining that the eye locations being blocked include:
Face's key position attitude data is fitted using least square method, obtains line function in head；
Using the eye key position filtered out as benchmark position, the eye being blocked is gone out according to the head middle line Function Mapping and is closed
Key position determines the eye locations being blocked with this.
7. a kind of self-service examination machine eyesight detects Intelligent processing device, which is characterized in that including obtain module, identification module,
It determines eyeglasses-wearing module, determine that eye blocks module, eyesight detection module；
Wherein, the acquisition module is for obtaining monitoring video information in physical examination cabin；
Identification module physical examination number in physical examination cabin for identification；
The determining eyeglasses-wearing module is used to determine the lens wear situation of the examinee；
The determining eye blocks module for determining the eye circumstance of occlusion of the examinee；
The eyesight detection module is used to carry out eyesight detection to the examinee.
8. device according to claim 7, which is characterized in that the identification module includes human body attitude data computation module
And analysis module；Wherein,
The human body attitude data computation module is used for according to default video frame speed, based on human body attitude identification model to the view
Frequency information carries out the identification of human body key position, obtains human body attitude data；
The analysis module judges physical examination number in physical examination cabin, completes physical examination number for analyzing the human body attitude data
Identification.
9. device according to claim 7, which is characterized in that the determining eyeglasses-wearing module includes human face image information
Obtain module, model computation module；Wherein,
The human face image information obtains the face figure that module is used to obtain examinee in the monitoring video information in physical examination cabin
As information；
The model computation module is used to for the human face image information to be input to eyeglasses-wearing disaggregated model trained in advance, in advance
It surveys whether the examinee wears glasses, determines the lens wear situation of the examinee.
10. device according to claim 8, which is characterized in that it includes that blocking position is true that the determining eye, which blocks module,
Cover half block, model prediction module, ratio calculation module, judgment module；Wherein,
The blocking position determining module is for determining the eye locations being blocked；
The model prediction module is used to for the human face image information in the monitoring video information to be input to screening trained in advance
Board position model is covered, predicts the position of eye-shading plate；
The ratio calculation module is used to calculate the ratio between the eye locations being blocked and the position of the cover plate；
For judging that the ratio is greater than preset threshold, the eye of the examinee blocks correctly the judgment module；
The blocking position determining module includes middle line function computation module, maps determining module；
The middle line function computation module is used to be fitted face's key position attitude data using least square method, obtains
Line function in head；
The mapping determining module is used for using the eye key position filtered out as benchmark position, according to line function in the head
The eye key position being blocked is mapped out, the eye locations being blocked are determined with this.
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