CN108021875A - A kind of vehicle driver's personalization fatigue monitoring and method for early warning - Google Patents
A kind of vehicle driver's personalization fatigue monitoring and method for early warning Download PDFInfo
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- CN108021875A CN108021875A CN201711206462.9A CN201711206462A CN108021875A CN 108021875 A CN108021875 A CN 108021875A CN 201711206462 A CN201711206462 A CN 201711206462A CN 108021875 A CN108021875 A CN 108021875A
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/59—Context or environment of the image inside of a vehicle, e.g. relating to seat occupancy, driver state or inner lighting conditions
- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- 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
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Abstract
The present invention relates to vehicle driver's personalization fatigue monitoring and method for early warning, belongs to active safety systems of vehicles field.A kind of vehicle driver's personalization fatigue monitoring and method for early warning, it is characterised in that：Gather driver personalityization feature, it is recorded in vehicle database, driver's facial characteristics is gathered in vehicle operation, is compared with driver personalityization feature in database, the integrated judgment of driver fatigue is carried out, and to more than default value when carries out early warning.The present invention can carry out visual information quantization analysis to driver under the conditions of a variety of driving environments, different accessories wearing conditions and different Characteristics of Drivers ' Behavior differences, the fatigue strength for preferably obtaining driver judges result, and fatigue driving early warning can be carried out, ensure that driver carries out safe driving, compared with prior art with universal adaptability and more preferable monitoring and warning effect.
Description
Technical field
The present invention relates to a kind of fatigue monitoring and method for early warning, more particularly to a kind of vehicle driver's personalization fatigue monitoring
And method for early warning.
Background technology
With the high speed development of social economy, automobile quantity and driver's quantity increase sharply, and automobile gives people's life
While work brings convenient, many traffic accidents are also triggered.Traffic safety has become the hot spot that society pays close attention to for a long time.
Fatigue driving is to trigger the major reason of major traffic accidents, its major traffic accidents triggered account for the 40% of accident total amount with
On.If it is possible to detect the fatigue state of driver and give warning in advance, then it can to a certain extent reduce and be driven by fatigue
The accident rate of initiation is sailed, guarantee is provided for driver safety.
Fatigue driving monitoring is that the specificity such as a kind of psychological to physiological driver during traveling and vehicle running state refer to
Mark is detected, and determine driver whether the means of fatigue driving.Current generally accepted detection method is divided into following 3 kinds：
1) using the physiological change index of biosensor detection driver, such as brain electricity, electrocardio, heart rate, breathing, myoelectricity
Deng；
2) using the vehicle running state variation characteristic of onboard sensor detection driver's driving behavior and its generation, such as turn
To, throttle, gear, brake, and the position of speed, acceleration, vehicle in track etc.；
3) using machine vision technique or the external change feature of other sensors technology for detection driver, as eyelid blinks
Move, nod, yawn.
In above-mentioned three kinds of fatigue detection methods, physical signs most can accurately detect driver fatigue, but the party
Method needs to install physiological signal sensor with driver, the restriction normal driver behavior of driver and posture, nothing
Method widespread adoption in actual scene.
The method detected using vehicle traveling information as fatigue strength, it is only necessary to utilize existing sensor on car.But this method is only
In degree of fatigue seriously to can just play forewarning function when accident will occurring, delay degree is high, can not be in fatigue driving
Early warning is given during in early stage state, therefore it is very narrow to leave the time window that driver makes a response for, driver hear it is pre-
The generation of accident can not be probably avoided when alert.
The method detected using driver's facial characteristics as fatigue strength takes into account the advantage of above two means.On the one hand drive
Member's fatigue state more can intuitively show facial characteristics and the head poses such as eyes (blink), face (yawning)
On (nodding), another aspect onboard sensor can effectively avoid the accident that fatigue driving triggers under certain environment.1994,
Repetition test and demonstration are passed through by Carnegie Mellon University, it is proposed that measure fatigue physical quantity " PERCLOS ", define for unit when
Time shared by interior (generally taking 1 minute or 30 seconds) eyes closed certain proportion (70% or 80%).The United States Federal's highway
Management board (FHWA) and National Highway Traffic safety management bureau (NHTSA) drive simulating in the lab, complete nine kinds
The comparison of fatigue detecting index, the results show that PERCLOS (percentage of eyelid closure over the
Pupil over time) it is best with the correlation of driving fatigue.
There are many inventions using PERCLOS as fatigue strength evaluation index at present, but PERCLOS also has its shortcoming,
The facial characteristics and motor habit of different people are all different, not only drive every time and are required for the more wasteful time of resampling,
If someone natively looks smug or conceited, little trick also be easy to cause erroneous judgement.
The content of the invention
The purpose of the application is to propose a kind of personalization fatigue prison based on driver's facial characteristics, head pose information
Survey method for early warning.System can be according to the custom of different drivers and the individualized feature of driver itself, at driver
Automatic early-warning is carried out when fatigue state, so as to reduce the traffic accident incidence triggered by driver tired driving.
The present invention is achieved through the following technical solutions：
A kind of vehicle driver's personalization fatigue monitoring and method for early warning, it is characterised in that：To driver personalityization feature
It is acquired, is recorded in vehicle database, driver's facial characteristics is gathered in vehicle operation, with being driven in database
Member's individualized feature is compared, and carries out the integrated judgment of driver fatigue, and to more than default value when carries out early warning.
The step of using personalized fatigue monitoring and method for early warning, is as follows：
Step 1, carry out driver personalityization collection apparatus, input database；
Step 2, driver prepare drive before, loading of databases；
Step 3, carry out face monitoring, and vehicle launch is not allowed if not detecting successfully；Allow vehicle if detecting successfully
Start, and driver's facial characteristics was gathered every 30-60 seconds, at the same time；
Step 4, will the driver's facial characteristics point location and image normalization that collect processing after, carry out recognition of face,
Head pose is calculated towards calculating, the differentiation of eye state and PERCLOS indexs；
Step 5, according to recognition of face as a result, if in database there are driver personalityization feature if use the number
According to, then use default default data if there is no driver personalityization feature, comprehensive head pose, eye state and
PERCLOS indexs compare and analyze driver's fatigue degree, and the fatigue strength for obtaining driver judges result；
If step 6, driver fatigue return to step 3, if driver fatigue not less than the early warning value of default
Degree then reminds driver to pay attention to more than the early warning value of default by the alarm mode of sound, vibrations.
Further, the step of driver personalityization feature is gathered described in step 1 is as follows：
Step 1a, Face datection is carried out to driver；
Step 1b, positioning feature point is carried out to the facial characteristics detected to handle with image normalization；
Step 1c, eye opening image, eye closing image and normal driving pose presentation are gathered, eyelid when opening eyes and closing one's eyes is calculated and puts down
Equal distance, head pose and direction when calculating normal driving；
Step 1d, the individualized feature of collection is stored in database.
Further, the particular content of the image normalization processing described in step 4 is included under varying environment illumination
Driver's image has driver's image under different accessories states to carry out intelligent characteristic through row brightness normalized and to wearing
Extraction.
Further, the head pose described in step 5, eye state and PERCLOS indexs include：Driver's single regards
Line deviates the duration, the number that sight deviates in driver 30 seconds, the upper palpebra inferior of driver are yawned behavior apart from, driver
Number, driver head is regular swings up and down the time.
Beneficial effect：
1) driver personalityization fatigue strength monitoring and pre-alarming method of the invention, is added by gathering driver personalityization feature
Database, and comprehensive individualized feature, head pose, eye state and PERCLOS indexs carry out driver's fatigue degree pair
Than analysis, the fatigue strength that can preferably obtain driver is judged as a result, compared with prior art with universal adaptability and more
Good monitoring and warning effect.
2) present invention, can be to driver in a variety of driving environments, different accessories using means such as graphics process, quantitative analysis
Visual information quantization analysis is carried out under the conditions of wearing condition and different Characteristics of Drivers ' Behavior differences, and it is pre- to carry out fatigue driving
It is alert, it is ensured that driver carries out safe driving.
Brief description of the drawings
Fig. 1 present invention carries out fatigue monitoring and the flow chart of early warning；
Fig. 2 gathers the flow chart of driver personalityization feature；
Embodiment
The present invention is described in detail with specific embodiment below in conjunction with the accompanying drawings：
As shown in Figure 1 a kind of vehicle driver's personalization fatigue monitoring and method for early warning, to driver personalityization feature into
Row collection, is recorded in vehicle database, driver's facial characteristics is gathered in vehicle operation, with driver in database
Individualized feature is compared, and carries out the integrated judgment of driver fatigue, and to more than default value when carries out early warning.
The step of using personalized fatigue monitoring and method for early warning, is as follows：
Step 1, carry out driver personalityization collection apparatus, input database；
Step 2, driver prepare drive before, loading of databases；
Step 3, carry out face monitoring, and vehicle launch is not allowed if not detecting successfully；Allow vehicle if detecting successfully
Start, and driver's facial characteristics was gathered every 30-60 seconds, at the same time；
Step 4, will the driver's facial characteristics point location and image normalization that collect processing after, carry out recognition of face,
Head pose is calculated towards calculating, the differentiation of eye state and PERCLOS indexs；
Step 5, according to recognition of face as a result, if in database there are driver personalityization feature if use the number
According to, then use default default data if there is no driver personalityization feature, comprehensive head pose, eye state and
PERCLOS indexs compare and analyze driver's fatigue degree, and the fatigue strength for obtaining driver judges result；
If step 6, driver fatigue return to step 3, if driver fatigue not less than the early warning value of default
Degree then reminds driver to pay attention to more than the early warning value of default by the alarm mode of sound, vibrations.
As shown in Figure 1, the step of collection driver personalityization feature, is as follows：
Step 1a, Face datection is carried out to driver；
Step 1b, positioning feature point is carried out to the facial characteristics detected to handle with image normalization；
Step 1c, eye opening image, eye closing image and normal driving pose presentation are gathered, eyelid when opening eyes and closing one's eyes is calculated and puts down
Equal distance, head pose and direction when calculating normal driving；
Step 1d, the individualized feature of collection is stored in database.
The flow of driver's facial image acquisition is：Original image is analyzed and is handled, obtains the position of human face region
Put, carry out the positioning of face key feature points, mark out the crucial geometric position such as eyes, eyebrow, nose, face, face mask,
And the rotation and scaling correction that face is normalized, obtain head pose information；At the same time the state of eyes open closing
The statistic of classification of eye, and by head pose information and facial characteristics with identity information it is corresponding after be entered into database.
The particular content of image normalization processing is included to driver's image under varying environment illumination through row brightness normalizing
Change processing and there is driver's image under different accessories states to carry out intelligent characteristic extraction wearing.
Head pose, eye state and PERCLOS indexs include：Driver's single sight deviates duration, driver
Sight deviates in 30 seconds number, the upper palpebra inferior of driver are regular apart from yawn behavior number, driver head of, driver
Swing up and down the time.
In vehicle travel process, classification that system opens and closes eyes the state of driver's eyes in the image that collects
Differentiate, count closed-eye time and PERCLOS fatigue indexes and common fatigue driving situation.Recognition of face and individualized feature mould
Block is matched with registered user in database, obtained and active user couple by carrying out feature extraction to human face region
The personalized fatigue characteristic answered, and as the discriminant criterion of the user's fatigue strength, the comprehensive distinguishing for fatigue strength.
At the beginning of vehicle launch, system at once differentiates driver identity information.In driver's driving procedure, this
Fatigue monitoring and method for early warning will monitor in real time with driving procedure, and give early warning when fatigue state occurs in driver.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all originals in the present invention
Then with all any modification, equivalent and improvement made within spirit etc., it should all be included in the protection scope of the present invention.
Claims (5)
1. a kind of vehicle driver's personalization fatigue monitoring and method for early warning, it is characterised in that：Driver personalityization feature is gathered,
It is recorded in vehicle database, driver's facial characteristics is gathered in vehicle operation, with driver personalityization in database
Feature is compared, and carries out the integrated judgment of driver fatigue, and to more than default value when carries out early warning.
2. vehicle driver's personalization fatigue monitoring as claimed in claim 1 and method for early warning, it is characterised in that：
The personalization fatigue monitoring and method for early warning use the following steps：
Step 1, collection driver personalityization feature, input database；
Step 2, driver prepare drive before, loading of databases；
Step 3, carry out Face datection, and vehicle launch is not allowed if not detecting successfully；Allow vehicle launch if detecting successfully,
And gathered driver's facial characteristics every 30-60 seconds；
Step 4, after handling the driver's facial characteristics point location and image normalization that collect, carry out recognition of face, head
Posture is calculated towards calculating, the differentiation of eye state and PERCLOS indexs；
Step 5, according to recognition of face as a result, if in database there are driver personalityization feature if use the data, such as
Fruit then to use default default data there is no driver personalityization feature, and comprehensive head pose, eye state and PERCLOS refer to
Mark compares and analyzes driver's fatigue degree, and the fatigue strength for obtaining driver judges result；
If step 6, driver fatigue return to step 3, if driver fatigue surpasses not less than the early warning value of default
The early warning value of default is crossed, then reminds driver to pay attention to by the alarm mode of sound, vibrations.
3. vehicle driver's personalization fatigue monitoring as claimed in claim 2 and method for early warning, it is characterised in that：
The step of driver personalityization feature is gathered described in step 1 is as follows：
Step 1a, Face datection is carried out to driver；
Step 1b, positioning feature point is carried out to the facial characteristics detected to handle with image normalization；
Step 1c, eye opening image, eye closing image and normal driving pose presentation are gathered, calculates eyelid average departure when opening eyes and closing one's eyes
From head pose and direction when calculating normal driving；
Step 1d, the individualized feature of collection is stored in database.
4. vehicle driver's personalization fatigue monitoring as claimed in claim 2 and method for early warning, it is characterised in that：
The particular content of image normalization processing described in step 4 is included to driver's image under varying environment illumination through row
Brightness normalized and there is driver's image under different accessories states to carry out intelligent characteristic extraction wearing.
5. vehicle driver's personalization fatigue monitoring as claimed in claim 2 and method for early warning, it is characterised in that：
Head pose, eye state and PERCLOS indexs described in step 5 include：When driver's single sight deviates lasting
Between, the number that sight deviates in driver 30 seconds, the upper palpebra inferior of driver yawn behavior number, driver apart from, driver
Head is regular to swing up and down the time.
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