CN107909772A - A kind of intelligence vehicle-mounted fatigue monitoring method - Google Patents
A kind of intelligence vehicle-mounted fatigue monitoring method Download PDFInfo
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Abstract
The present invention provides a kind of intelligent vehicle-mounted fatigue monitoring system, it is related to field of automobile safety.It is characterized in that, the system comprises：Vehicle-mounted end, high in the clouds, users' mobile end and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensing system, central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and cloud processor；The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Mobile terminal data transmission module and mobile terminal operation interface.The invention have monitoring it is accurate, it is reliable, possess the advantages that big data analysis and monitoring function, improvement traditional algorithm, algorithm trap.
Description
Technical field
The present invention relates to field of automobile safety, more particularly to a kind of intelligent vehicle-mounted fatigue monitoring system.
Background technology
How for driver the weight that an effectively practical safety driving assist system is safe driving of vehicle is provided
Want problem.Fatigue-driving detection technology based on machine vision at home and abroad carried out it is widely studied, wherein with driver eye
The detection of portion's feature is the most extensive.
We can encounter that length of one's sleep the previous day is very few, and sleep quality is excessively poor during daily driving；The very good cause of road conditions
Make surface conditions single；Run into dust storm, rain, mist, snow weather conditions；For a long time, drive a vehicle over long distances；Speed is too fast or excessively slow；Arrive
Up to situations such as destination having time limitation etc., these all can be the factor for inducing your fatigue driving, or even outside vehicle itself or car
Noise and serious vibration；Seat, which adjusts the reason such as improper, can all cause your fatigue driving, so as to induce traffic accident.
Occur when driving gear shift not in time, it is inaccurate, illustrate that human body is in slight fatigue state；It is dynamic when there is operation
Make dull, can even forget the operation that will carry out sometimes, illustrate that human body is tired in moderate；When occur subconscious operation or
When there is short time sleep phenomenon, illustrate that human body in severe fatigue, has just been bred disaster when often waking up.In emergency situation
Before generation, the initial sign of fatigue driving can be detected, and the alarm tone at this moment to sound is exactly often to drive
The best opportunity that member saves out from Death's hand.
Existing fatigue monitoring system is primarily present the defects of following：
1st, monitoring is inaccurate：Existing fatigue monitoring method, otherwise it is monitored using sensor, otherwise utilize merely
Image procossing.And both often all there are it is certain the defects of, it is very inaccurate to frequently result in monitoring result, so as to produce wrong report
It is alert.
2nd, big data analysis is lacked：Since many driver tired drivings are the processes of a chronicity, if without pin
The analysis of chronicity is carried out to driver.Also lack third party to exercise supervision to driver management, cause many chronic fatigues to be driven
The driver sailed cannot obtain it is watchful, so as to cannot prevent trouble before it happens.
3rd, algorithm falls behind：Existing fatigue monitoring method, mostly using old image algorithm.And due to fatigue monitoring and
Common image recognition has very big otherness.If using old algorithm simply, it can directly cause monitoring result inaccuracy and algorithm
Complexity, deals with very slow.
The content of the invention
In consideration of it, the present invention provides a kind of intelligent vehicle-mounted fatigue monitoring system, the invention have monitoring it is accurate, can
Lean on, possess the advantages that big data analysis and monitoring function, improvement traditional algorithm, algorithm trap.The technical solution adopted by the present invention
It is as follows：
A kind of intelligence vehicle-mounted fatigue monitoring system, it is characterised in that the system comprises：Vehicle-mounted end, high in the clouds, Yong Huyi
Moved end and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensor system
System, central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and high in the clouds processing
Device；The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Move
Moved end data transmission module and mobile terminal operation interface；
The video acquisition module signal is connected to coded treatment module；The coded treatment module by signal is connected to algorithm
Processing module；The algorithm processing module signal is connected to central processing unit；Signal is connected to behaviour to the central processing unit respectively
Make interface, sensing system, prior-warning device and data transmission device；The data transmission device signal is connected to high in the clouds data and passes
Defeated device；Signal is connected to mobile terminal data transmission module, cloud processor and supervision to the high in the clouds data transmission device respectively
Department's processor；The mobile terminal data transmission module signal is connected to mobile terminal operation interface；The cloud processor signal
It is connected to supervision department's data transmission device；Supervision department's data transmission device signal is connected to supervision department's processor；
The video acquisition module, for gathering the facial image information of driver, the image information got is sent
To coded treatment module；The coded treatment module is used to carry out the facial image information received at digital video decoding
Reason, the facial image information after processing is sent to algorithm processing module；The algorithm processing module, for passing through image recognition
Algorithm docks received facial image information and carries out algorithm process, judges the state of mind of driver, will determine that result send to
Central processing unit；The sensing system, including：Blood oxygen transducer and acidity-basicity sensor；The blood oxygen transducer is used for real
When monitor human body blood oxygenation information, the blood oxygenation information monitored is sent to central processing unit；The acidity-basicity sensor is used for
The pH value on human skin is monitored, the pH value monitored is sent to central processing unit；The central processing unit, is used
In judging whether driver is currently in fatigue state according to the data message received, it will determine that result is respectively sent to operate
Interface, prior-warning device and data transmission device；The operation interface, for showing judging result；The prior-warning device, for root
It is judged that as a result issue warning signal；
The image-recognizing method that the algorithm processing module uses comprises the following steps：
Step 1：The Face datection based on colour of skin cluster is carried out to the image information that coded treatment module sends over；
Step 2：According to Face datection as a result, being distributed according to eyes in the geometric position of face, the big position approximate of eyes is determined
Put, reduce the regional extent of eyeball detection；
Step 3：In the eyeball search range of diminution, edge detection is carried out to ocular using edge detection algorithm,
The marginal information of eye is extracted, and binary conversion treatment is carried out to it, then Connected component point is carried out to the ocular after binaryzation
Analysis, the interference of circumference of eyes picture noise point is removed using the bianry image filtering method based on regional connectivity；
Step 4：According to improved integral projection algorithm, the distance of left and right canthus and upper palpebra inferior is calculated, determines driver
Eyes open closed state；
Step 5：The flat rate of definition blink, and the detection of fatigue driving is realized accordingly.
The cloud processor, for by the data information memory that high in the clouds data transmission device sends in local, often
Every some cycles, big data statistical analysis is carried out to being stored in local data message, analysis result is sent to supervision department
Data transmission device；And receive the assessment result from supervision end, assessment result is stored in local, periodically ties the assessment
Fruit is sent to users' mobile end.
Supervision department's processor, for the big data statistic analysis result sended over according to high in the clouds, to the driving
The state of mind of personnel is artificially assessed, and assessment result is sent to cloud processor through high in the clouds data transmission device and is deposited
Storage.
The users' mobile end operation interface, the assessment result for high in the clouds to be sended over are shown.
The method for detecting human face based on colour of skin cluster comprises the following steps：
Step 1：The image collected from camera is rgb format, is by RGB format conversions using equation below realization
Step 2：Equation below is recycled to detect the area of skin color of driver：
R ＞ G＆＆ | R-G | >=11
340≤S≤359P0≤S≤50
0.12≤T≤0.7&&0.3≤V≤1.0
Step 3：Then horizontal and vertical projection is carried out to face, determines the borderline region of face, border determines formula such as
Under：
Wherein, h, w are respectively the height and width with the horizontal and vertical face area for projecting and trying to achieve.
The method that the eye area-of-interest determines comprises the following steps：
Step 1：Assuming that the human face region length detected is HF；Width is WF；
Step 2：In several vertical direction, eyes are located at more than face's half, the region of HF/5 below the crown.
Step 3：In the horizontal direction, eye borderline region must be positioned at apart from face's left margin, WF/8 go out to start to away from
From the region at eye right margin WF/8.
Using above technical scheme, present invention produces following beneficial effect：
1st, monitoring is accurate：The fatigue monitoring system that the present invention uses, has monitoring accurate, and image monitoring is gathered except using
Beyond main judge means, sensing system is also added as auxiliary monitoring.Two monitoring results are integrated into work
To judge means, the accuracy and science of monitoring can be greatly promoted.
2nd, big data analysis：The concept of big data and cloud storage is referred to fatigue monitoring by the present invention.Except for
Single fatigue driving is carried out beyond early warning, can also be directed to multiple fatigue driving data, be counted fatigue driving in a period of time
Number, different degrees of early warning is sent according to these data.
3rd, algorithm is advanced：The present invention improves traditional image recognition algorithm, has been substantially carried out gathering based on the colour of skin
Face datection, the eye area-of-interest of class determine, the edge detection based on Sobel operators, using improve integral projection algorithm
Determine the distance of left and right canthus and upper palpebra inferior.Identify the frequency of wink of driver by these, and according to frequency of wink come
The degree of fatigue of integrated judgment driver.
4th, possesses supervisory role：Big data handling result and supervision department are attached by the present invention, and supervision department can be with
Analysis of the real-time reception to the long-term state of mind of driver.And the data message of some high-risk drivers is extracted, and by supervising
Department provides alert, and prevention effect is just played when danger is sent.
Brief description of the drawings
Fig. 1 is a kind of intelligent vehicle-mounted fatigue monitoring system structure diagram of the present invention.
Embodiment
All features disclosed in this specification, or disclosed all methods or during the step of, except mutually exclusive
Feature and/or step beyond, can combine in any way.
Any feature disclosed in this specification (including any accessory claim, summary), unless specifically stated,
Replaced by other equivalent or with similar purpose alternative features.I.e., unless specifically stated, each feature is a series of
An example in equivalent or similar characteristics.
A kind of intelligent vehicle-mounted fatigue monitoring system is provided in the embodiment of the present invention 1, system structure is as shown in Figure 1：
A kind of intelligence vehicle-mounted fatigue monitoring system, it is characterised in that the described method includes：Vehicle-mounted end, high in the clouds, Yong Huyi
Moved end and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensor system
System, central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and high in the clouds processing
Device；The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Move
Moved end data transmission module and mobile terminal operation interface；
The video acquisition module signal is connected to coded treatment module；The coded treatment module by signal is connected to algorithm
Processing module；The algorithm processing module signal is connected to central processing unit；Signal is connected to behaviour to the central processing unit respectively
Make interface, sensing system, prior-warning device and data transmission device；The data transmission device signal is connected to high in the clouds data and passes
Defeated device；Signal is connected to mobile terminal data transmission module, cloud processor and supervision to the high in the clouds data transmission device respectively
Department's processor；The mobile terminal data transmission module signal is connected to mobile terminal operation interface；The cloud processor signal
It is connected to supervision department's data transmission device；Supervision department's data transmission device signal is connected to supervision department's processor.
The video acquisition module, for gathering the facial image information of driver, the image information got is sent
To coded treatment module；The coded treatment module is used to carry out the facial image information received at digital video decoding
Reason, the facial image information after processing is sent to algorithm processing module；The algorithm processing module, for passing through image recognition
Algorithm docks received facial image information and carries out algorithm process, judges the state of mind of driver, will determine that result send to
Central processing unit；The sensing system, including：Blood oxygen transducer and acidity-basicity sensor；The blood oxygen transducer is used for real
When monitor human body blood oxygenation information, the blood oxygenation information monitored is sent to central processing unit；The acidity-basicity sensor is used for
The pH value on human skin is monitored, the pH value monitored is sent to central processing unit；The central processing unit, is used
In judging whether driver is currently in fatigue state according to the data message received, it will determine that result is respectively sent to operate
Interface, prior-warning device and data transmission device；The operation interface, for showing judging result；The prior-warning device, for root
It is judged that as a result issue warning signal.
The cloud processor, for by the data information memory that high in the clouds data transmission device sends in local, often
Every some cycles, big data statistical analysis is carried out to being stored in local data message, analysis result is sent to supervision department
Data transmission device；And receive the assessment result from supervision end, assessment result is stored in local, periodically ties the assessment
Fruit is sent to users' mobile end.
Supervision department's processor, for the big data statistic analysis result sended over according to high in the clouds, to the driving
The state of mind of personnel is artificially assessed, and assessment result is sent to cloud processor through high in the clouds data transmission device and is deposited
Storage.
The users' mobile end operation interface, the assessment result for high in the clouds to be sended over are shown.
A kind of intelligent vehicle-mounted fatigue monitoring system is provided in the embodiment of the present invention 2, system construction drawing is as described in Figure 1：
A kind of intelligence vehicle-mounted fatigue monitoring system, it is characterised in that the described method includes：Vehicle-mounted end, high in the clouds, Yong Huyi
Moved end and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensor system
System, central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and high in the clouds processing
Device；The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Move
Moved end data transmission module and mobile terminal operation interface；
The video acquisition module signal is connected to coded treatment module；The coded treatment module by signal is connected to algorithm
Processing module；The algorithm processing module signal is connected to central processing unit；Signal is connected to behaviour to the central processing unit respectively
Make interface, sensing system, prior-warning device and data transmission device；The data transmission device signal is connected to high in the clouds data and passes
Defeated device；Signal is connected to mobile terminal data transmission module, cloud processor and supervision to the high in the clouds data transmission device respectively
Department's processor；The mobile terminal data transmission module signal is connected to mobile terminal operation interface；The cloud processor signal
It is connected to supervision department's data transmission device；Supervision department's data transmission device signal is connected to supervision department's processor.
The video acquisition module, for gathering the facial image information of driver, the image information got is sent
To coded treatment module；The coded treatment module is used to carry out the facial image information received at digital video decoding
Reason, the facial image information after processing is sent to algorithm processing module；The algorithm processing module, for passing through image recognition
Algorithm docks received facial image information and carries out algorithm process, judges the state of mind of driver, will determine that result send to
Central processing unit；The sensing system, including：Blood oxygen transducer and acidity-basicity sensor；The blood oxygen transducer is used for real
When monitor human body blood oxygenation information, the blood oxygenation information monitored is sent to central processing unit；The acidity-basicity sensor is used for
The pH value on human skin is monitored, the pH value monitored is sent to central processing unit；The central processing unit, is used
In judging whether driver is currently in fatigue state according to the data message received, it will determine that result is respectively sent to operate
Interface, prior-warning device and data transmission device；The operation interface, for showing judging result；The prior-warning device, for root
It is judged that as a result issue warning signal.
The cloud processor, for by the data information memory that high in the clouds data transmission device sends in local, often
Every some cycles, big data statistical analysis is carried out to being stored in local data message, analysis result is sent to supervision department
Data transmission device；And receive the assessment result from supervision end, assessment result is stored in local, periodically ties the assessment
Fruit is sent to users' mobile end.
Supervision department's processor, for the big data statistic analysis result sended over according to high in the clouds, to the driving
The state of mind of personnel is artificially assessed, and assessment result is sent to cloud processor through high in the clouds data transmission device and is deposited
Storage.
The users' mobile end operation interface, the assessment result for high in the clouds to be sended over are shown.
The image-recognizing method that the algorithm processing module uses comprises the following steps：
Step 1：The Face datection based on colour of skin cluster is carried out to the image information that coded treatment module sends over；
Step 2：According to Face datection as a result, being distributed according to eyes in the geometric position of face, the big position approximate of eyes is determined
Put, reduce the regional extent of eyeball detection；
Step 3：In the eyeball search range of diminution, edge detection is carried out to ocular using edge detection algorithm,
The marginal information of eye is extracted, and binary conversion treatment is carried out to it, then Connected component point is carried out to the ocular after binaryzation
Analysis, the interference of circumference of eyes picture noise point is removed using the bianry image filtering method based on regional connectivity；
Step 4：According to improved integral projection algorithm, the distance of left and right canthus and upper palpebra inferior is calculated, determines driver
Eyes open closed state；
Step 5：The flat rate of definition blink, and the detection of fatigue driving is realized accordingly.
The method for detecting human face based on colour of skin cluster comprises the following steps：
Step 1：The image collected from camera is rgb format, is realized using equation below and is converted to rgb format
Step 2：Equation below is recycled to detect the area of skin color of driver：
R ＞ G＆＆ | R-G | >=11
340≤S≤359P0≤S≤50
0.12≤T≤0.7&&0.3≤V≤1.0
Step 3：Then horizontal and vertical projection is carried out to face, determines the borderline region of face, border determines formula such as
Under：
Wherein, h, w are respectively the height and width with the horizontal and vertical face area for projecting and trying to achieve.
The method that the eye area-of-interest determines comprises the following steps：
Step 1：Assuming that the human face region length detected is HF；Width is WF；
Step 2：In several vertical direction, eyes are located at more than face's half, the region of HF/5 below the crown.
Step 3：In the horizontal direction, eye borderline region must be positioned at apart from face left margin WF/8 go out to start to away from
From the region at eye right margin WF/8.
A kind of intelligent vehicle-mounted fatigue monitoring system is provided in the embodiment of the present invention 3, system structure is as shown in Figure 1：
A kind of intelligence vehicle-mounted fatigue monitoring system, it is characterised in that the described method includes：Vehicle-mounted end, high in the clouds, Yong Huyi
Moved end and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensor system
System, central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and high in the clouds processing
Device；The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Move
Moved end data transmission module and mobile terminal operation interface；
The video acquisition module signal is connected to coded treatment module；The coded treatment module by signal is connected to algorithm
Processing module；The algorithm processing module signal is connected to central processing unit；Signal is connected to behaviour to the central processing unit respectively
Make interface, sensing system, prior-warning device and data transmission device；The data transmission device signal is connected to high in the clouds data and passes
Defeated device；Signal is connected to mobile terminal data transmission module, cloud processor and supervision to the high in the clouds data transmission device respectively
Department's processor；The mobile terminal data transmission module signal is connected to mobile terminal operation interface；The cloud processor signal
It is connected to supervision department's data transmission device；Supervision department's data transmission device signal is connected to supervision department's processor.
The video acquisition module, for gathering the facial image information of driver, the image information got is sent
To coded treatment module；The coded treatment module is used to carry out the facial image information received at digital video decoding
Reason, the facial image information after processing is sent to algorithm processing module；The algorithm processing module, for passing through image recognition
Algorithm docks received facial image information and carries out algorithm process, judges the state of mind of driver, will determine that result send to
Central processing unit；The sensing system, including：Blood oxygen transducer and acidity-basicity sensor；The blood oxygen transducer is used for real
When monitor human body blood oxygenation information, the blood oxygenation information monitored is sent to central processing unit；The acidity-basicity sensor is used for
The pH value on human skin is monitored, the pH value monitored is sent to central processing unit；The central processing unit, is used
In judging whether driver is currently in fatigue state according to the data message received, it will determine that result is respectively sent to operate
Interface, prior-warning device and data transmission device；The operation interface, for showing judging result；The prior-warning device, for root
It is judged that as a result issue warning signal.
The cloud processor, for by the data information memory that high in the clouds data transmission device sends in local, often
Every some cycles, big data statistical analysis is carried out to being stored in local data message, analysis result is sent to supervision department
Data transmission device；And receive the assessment result from supervision end, assessment result is stored in local, periodically ties the assessment
Fruit is sent to users' mobile end.
Supervision department's processor, for the big data statistic analysis result sended over according to high in the clouds, to the driving
The state of mind of personnel is artificially assessed, and assessment result is sent to cloud processor through high in the clouds data transmission device and is deposited
Storage.
The users' mobile end operation interface, the assessment result for high in the clouds to be sended over are shown.
The image-recognizing method that the algorithm processing module uses comprises the following steps：
Step 1：The Face datection based on colour of skin cluster is carried out to the image information that coded treatment module sends over；
Step 2：According to Face datection as a result, being distributed according to eyes in the geometric position of face, the big position approximate of eyes is determined
Put, reduce the regional extent of eyeball detection；
Step 3：In the eyeball search range of diminution, edge detection is carried out to ocular using edge detection algorithm,
The marginal information of eye is extracted, and binary conversion treatment is carried out to it, then Connected component point is carried out to the ocular after binaryzation
Analysis, the interference of circumference of eyes picture noise point is removed using the bianry image filtering method based on regional connectivity；
Step 4：According to improved integral projection algorithm, the distance of left and right canthus and upper palpebra inferior is calculated, determines driver
Eyes open closed state；
Step 5：The flat rate of definition blink, and the detection of fatigue driving is realized accordingly.
The method for detecting human face based on colour of skin cluster comprises the following steps：
Step 1：The image collected from camera is rgb format, is realized using equation below and is converted to rgb format
Step 2：Equation below is recycled to detect the area of skin color of driver：
R ＞ G＆＆ | R-G | >=11
340≤S≤359P0≤S≤50
0.12≤T≤0.7&&0.3≤V≤1.0
Step 3：Then horizontal and vertical projection is carried out to face, determines the borderline region of face, border determines formula such as
Under：
Wherein, h, w are respectively the height and width with the horizontal and vertical face area for projecting and trying to achieve.
The method that the eye area-of-interest determines comprises the following steps：
Step 1：Assuming that the human face region length detected is HF；Width is WF；
Step 2：In several vertical direction, eyes are located at more than face's half, the region of HF/5 below the crown.
Step 3：In the horizontal direction, eye borderline region must be positioned at apart from face left margin WF/8 go out to start to away from
From the region at eye right margin WF/8.
The fatigue monitoring system that the present invention uses, it is accurate with monitoring, mainly commented except being used as using collection image monitoring
Sentence beyond means, also added sensing system as auxiliary monitoring.Two monitoring results are integrated as judge means,
The accuracy and science of monitoring can be greatly promoted.
The concept of big data and cloud storage is referred to fatigue monitoring by the present invention.Except for single fatigue driving into
Beyond row early warning, multiple fatigue driving data can also be directed to, the number of fatigue driving in a period of time are counted, according to these
Data send different degrees of early warning.
The present invention improves traditional image recognition algorithm, has been substantially carried out the face inspection based on colour of skin cluster
Survey, eye area-of-interest determine, the edge detection based on Sobel operators, using improve integral projection algorithm determine right and left eyes
Angle and the distance of upper palpebra inferior.The frequency of wink of driver is identified by these, and is driven according to frequency of wink come integrated judgment
The degree of fatigue for the person of sailing.
Big data handling result and supervision department are attached by the present invention, and supervision department can be with real-time reception to driver
The analysis of the long-term state of mind.And the data message of some high-risk drivers is extracted, and provided alert by supervision department, endangering
Prevention effect is just played when sending in danger.
The invention is not limited in foregoing embodiment.The present invention, which expands to, any in the present specification to be disclosed
New feature or any new combination, and disclose any new method or process the step of or any new combination.
Claims (6)
- A kind of 1. intelligence vehicle-mounted fatigue monitoring system, it is characterised in that the system comprises：Vehicle-mounted end, high in the clouds, user's movement End and supervision end；The vehicle-mounted end includes：Video acquisition module, coded treatment module, algorithm processing module, sensing system, Central processing unit and operation interface；The high in the clouds includes：Data transmission device, high in the clouds data sink and cloud processor； The supervision end includes：Supervision department's data transmission device and supervision department's processor；The users' mobile end includes：Mobile terminal Data transmission module and mobile terminal operation interface；The video acquisition module signal is connected to coded treatment module；The coded treatment module by signal is connected to algorithm process Module；The algorithm processing module signal is connected to central processing unit；Signal is connected to operation circle to the central processing unit respectively Face, sensing system, prior-warning device and data transmission device；The data transmission device signal is connected to high in the clouds data transfer dress Put；Signal is connected to mobile terminal data transmission module, cloud processor and supervision department to the high in the clouds data transmission device respectively Processor；The mobile terminal data transmission module signal is connected to mobile terminal operation interface；The cloud processor signal connection In supervision department's data transmission device；Supervision department's data transmission device signal is connected to supervision department's processor；The video acquisition module, for gathering the facial image information of driver, the image information got is sent to volume Code processing module；The coded treatment module is used to the facial image information received carrying out digital video decoding processing, Facial image information after processing is sent to algorithm processing module；The algorithm processing module, for being calculated by image recognition Method docks received facial image information and carries out algorithm process, judges the state of mind of driver, will determine that result is sent into Central processor；The sensing system, including：Blood oxygen transducer and acidity-basicity sensor；The blood oxygen transducer is used for real-time The blood oxygenation information of human body is monitored, the blood oxygenation information monitored is sent to central processing unit；The acidity-basicity sensor is used to supervise The pH value on human skin is surveyed, the pH value monitored is sent to central processing unit；The central processing unit, is used for Data message according to receiving judges whether driver is currently in fatigue state, will determine that result is respectively sent to operation circle Face, prior-warning device and data transmission device；The operation interface, for showing judging result；The prior-warning device, for basis Judging result issues warning signal；The image-recognizing method that the algorithm processing module uses comprises the following steps：Step 1：The Face datection based on colour of skin cluster is carried out to the image information that coded treatment module sends over；Step 2：According to Face datection as a result, being distributed according to eyes in the geometric position of face, the Position Approximate of eyes, contracting are determined The regional extent of microphthalmus detection；Step 3：In the eyeball search range of diminution, edge detection, extraction are carried out to ocular using edge detection algorithm The marginal information of eye, and binary conversion treatment is carried out to it, then Connected component analysis is carried out to the ocular after binaryzation, adopt The interference of circumference of eyes picture noise point is removed with the bianry image filtering method based on regional connectivity；Step 4：According to improved integral projection algorithm, the distance of left and right canthus and upper palpebra inferior is calculated, determines driver's eyes Open closed state；Step 5：The flat rate of definition blink, and the detection of fatigue driving is realized accordingly.
- 2. intelligence vehicle-mounted fatigue monitoring system as claimed in claim 1, it is characterised in that the cloud processor, is used for By the data information memory that high in the clouds data transmission device sends in local, every some cycles, to being stored in local number It is believed that breath carries out big data statistical analysis, analysis result is sent to supervision department's data transmission device；And receive from prison The assessment result of pipe end, is stored in local by assessment result, periodically sends the assessment result to users' mobile end.
- 3. intelligence vehicle-mounted fatigue monitoring system as claimed in claim 2, it is characterised in that supervision department's processor, For the big data statistic analysis result sended over according to high in the clouds, the state of mind of the driver is artificially assessed, Assessment result is sent to cloud processor through high in the clouds data transmission device and is stored.
- 4. intelligence vehicle-mounted fatigue monitoring system as claimed in claim 3, it is characterised in that the users' mobile end operates boundary Face, the assessment result for high in the clouds to be sended over are shown.
- 5. intelligence vehicle-mounted fatigue monitoring system as claimed in claim 1, it is characterised in that the people based on colour of skin cluster Face detecting method comprises the following steps：Step 1：The image collected from camera is rgb format, is realized using equation below and is converted to rgb format<mrow> <msub> <mi>S</mi> <mn>1</mn> </msub> <mo>=</mo> <msup> <mi>cos</mi> <mrow> <mo>-</mo> <mn>1</mn> </mrow> </msup> <mo>{</mo> <mfrac> <mrow> <mn>0.5</mn> <mo>*</mo> <mo>&lsqb;</mo> <mrow> <mo>(</mo> <mi>R</mi> <mo>-</mo> <mi>G</mi> <mo>)</mo> </mrow> <mo>+</mo> <mrow> <mo>(</mo> <mi>R</mi> <mo>-</mo> <mi>B</mi> <mo>)</mo> </mrow> <mo>&rsqb;</mo> </mrow> <msqrt> <mrow> <msup> <mrow> <mo>(</mo> <mi>R</mi> <mo>-</mo> <mi>G</mi> <mo>)</mo> </mrow> <mn>2</mn> </msup> <mo>+</mo> <mrow> <mo>(</mo> <mi>R</mi> <mo>-</mo> <mi>B</mi> <mo>)</mo> </mrow> <mo>*</mo> <mrow> <mo>(</mo> <mi>G</mi> <mo>-</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> </msqrt> </mfrac> <mo>}</mo> </mrow><mrow> <mi>T</mi> <mo>=</mo> <mfrac> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mo>)</mo> </mrow> <mo>-</mo> <mi>M</mi> <mi>i</mi> <mi>n</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> </mfrac> </mrow><mrow> <mi>J</mi> <mo>=</mo> <mfrac> <mrow> <mi>M</mi> <mi>a</mi> <mi>x</mi> <mrow> <mo>(</mo> <mi>R</mi> <mo>,</mo> <mi>G</mi> <mo>,</mo> <mi>B</mi> <mo>)</mo> </mrow> </mrow> <mn>255</mn> </mfrac> </mrow>Step 2：Equation below is recycled to detect the area of skin color of driver：R ＞ G＆＆ | R-G | >=11340≤S≤359P0≤S≤500.12≤T≤0.7&&0.3≤V≤1.0Step 3：Then horizontal and vertical projection is carried out to face, determines the borderline region of face, border determines that formula is as follows：<mrow> <mi>h</mi> <mo>=</mo> <mfenced open = "{" close = ""> <mtable> <mtr> <mtd> <mrow> <mn>1.25</mn> <mi>w</mi> </mrow> </mtd> <mtd> <mrow> <msup> <mi>h</mi> <mo>&prime;</mo> </msup> <mo>&NotElement;</mo> <mo>&lsqb;</mo> <mn>0.8</mn> <mi>w</mi> <mo>,</mo> <mn>1.4</mn> <mi>w</mi> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> <mtr> <mtd> <msup> <mi>h</mi> <mo>&prime;</mo> </msup> </mtd> <mtd> <mrow> <msup> <mi>h</mi> <mo>&prime;</mo> </msup> <mo>&Element;</mo> <mo>&lsqb;</mo> <mn>0.8</mn> <mi>w</mi> <mo>,</mo> <mn>1.4</mn> <mi>w</mi> <mo>&rsqb;</mo> </mrow> </mtd> </mtr> </mtable> </mfenced> </mrow>Wherein, h, w are respectively the height and width with the horizontal and vertical face area for projecting and trying to achieve.
- 6. intelligence vehicle-mounted fatigue monitoring system as claimed in claim 1, it is characterised in that the eye area-of-interest is true Fixed method comprises the following steps：Step 1：Assuming that the human face region length detected is HF；Width is WF；Step 2：In several vertical direction, eyes are located at more than face's half, the region of HF/5 below the crown；Step 3：In the horizontal direction, eye borderline region must be positioned at apart from face's left margin, and WF/8, which goes out, to be started to apart from eye Region at portion right margin WF/8.
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