CN107657236A - Vehicle security drive method for early warning and vehicle-mounted early warning system - Google Patents
Vehicle security drive method for early warning and vehicle-mounted early warning system Download PDFInfo
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- CN107657236A CN107657236A CN201710903267.5A CN201710903267A CN107657236A CN 107657236 A CN107657236 A CN 107657236A CN 201710903267 A CN201710903267 A CN 201710903267A CN 107657236 A CN107657236 A CN 107657236A
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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
- G06V20/59—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
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/02—Alarms for ensuring the safety of persons
- G08B21/06—Alarms for ensuring the safety of persons indicating a condition of sleep, e.g. anti-dozing alarms
Abstract
The invention discloses a kind of vehicle security drive method for early warning and vehicle-mounted early warning system, methods described to include：Build CNN deep learning frame models, the complex environment and driver-operated state index that drive simulating person drives；To driver's Face datection, face state in driver's driving procedure is judged；Driver tired driving behavior is detected；Driver safety driving behavior is detected；The temporal information or range information that real-time collection vehicle continuously drives；The positional information and running orbit of automobile are monitored, and timing identifies driver identity information.Driver's face-image of collection is compared by CNN deep learnings frame model by the present invention, judge the driving safety state of driver, algorithm operational efficiency is high, accuracy rate is high, hardware configuration requires low, it is practical, obtain the positional information and driving parameters of vehicle in real time by position system unit, realize the behavior management of driving to driver, the management of vehicle traveling, anti-lost car management.
Description
Technical field
The present invention relates to car steering early warning technology field, and CNN deep learning frame models are based on more particularly to one kind
Vehicle security drive method for early warning and vehicle-mounted early warning system.
Background technology
Vehicle security drive is a content being concerned about very much in people's daily life, is led in vehicle security drive early warning
Domain, the technical scheme that prior art generally uses for：First, needing to network, by way of artificial or machine detection of networking, sentence
Disconnected driver whether there is unsafe driving behavior, and this mode depends critically upon network, and larger, spy is limited by network transfer speeds
It is not that can not implement in the region that some do not have wireless network signal covering, this method；Second, image alignment algorithm is used, but
Be prior art algorithm it is general simpler, it is impossible to the fine facial status information for judging driver, such as Opencv are carried
Harr detection methods, it is difficult to complexity true environment in capture facial image, the significantly shifting of driver can only be judged
The status information of dynamic position, meanwhile, the image alignment algorithm cost of prior art is high, and False Rate is high.
Therefore, prior art needs to improve.
The content of the invention
A technical problem to be solved of the embodiment of the present invention is：A kind of vehicle security drive method for early warning and car are provided
Early warning system is carried, to solve problems of the prior art.
One side according to embodiments of the present invention, there is provided a kind of vehicle security drive method for early warning, including：
CNN deep learning frame models are built, the complex environment and driver-operated state that drive simulating person drives refer to
Mark；
Contraction CNN methods in CNN deep learning frame models, to driver's Face datection, judge that driver drives
Face state during sailing；
Driver tired driving behavior is detected according to CNN deep learnings frame model, the fatigue driving behavior
Closed state is opened for driver's eyes, opens and closes frequency, eye-closing period behavior；
Driver safety driving behavior is detected according to CNN deep learnings frame model, the safe driving behavior
The behavior in the transfer visual direction, both hands departure direction disk that are driver in driving procedure；
The temporal information or range information that real-time collection vehicle continuously drives, when continuously driving the time or continuous more than setting
During operating range, warning information is sent；
The positional information and running orbit of automobile are monitored, and timing identifies driver identity information, and driver is driven and gone
For and driver identity be managed.
In another embodiment based on above-mentioned vehicle security drive method for early warning, in addition to：
According to driver's driving condition image of collection, re -training CNN deep learning frame models.
It is described to driver's Face datection bag in another embodiment based on above-mentioned vehicle security drive method for early warning
Include：Detect face monochrome information, the information that tilts, downward shift information and the pitch information of driver.
It is described according to CNN deep learning frameworks in another embodiment based on above-mentioned vehicle security drive method for early warning
Model carries out detection to driver tired driving behavior to be included：
Continuously the eyes of analysis driver open closed state information in the range of setting time；
The eyes of calculating driver, which are opened, closes frequency and continuous eye-closing period；
Judge whether that the eyes that driver occurs are opened and close frequency reduction, and continuous eye-closing period exceedes setting time threshold value；
If it is, judging driver for fatigue driving, warning system carries out voice message.
It is described according to CNN deep learning frameworks in another embodiment based on above-mentioned vehicle security drive method for early warning
Model carries out detection to driver safety driving behavior to be included：
Continuously whether the driving condition of detection driver is unsafe driving state in the range of setting time, the uneasiness
Full driving condition includes：Driver receives calls, looked around in driver's driving procedure, driver in driver's driving procedure
Talked in driving procedure in both hands departure direction disk, driver's driving procedure with other people；
Judge whether the duration of driver's unsafe driving behavior exceedes setting time threshold value or occur dangerous
Whether the number of driving behavior exceedes setting frequency threshold value；
If it is, judging that driver drives vehicle for non-secure states, warning system carries out voice message.
Other side based on the embodiment of the present invention, a kind of onboard system of vehicle security drive early warning is also disclosed,
Including：
Main control unit, the main control unit are used to load CNN deep learning frame models, and the main control unit includes detection
Algoritic module, detection algorithm is taken, the driver's image and CNN deep learning frame models of collection are compared, judge driver
Driving condition, the detection algorithm include face recognition detection, eye recognition detection and Activity recognition detection；
Image acquisition units, described image collecting unit are connected with the main control unit, and the realtime graphic of collection is sent
To main control unit, image acquisition units gather the face face-image of driver in real time, and analyze driver's eyes state, head
Action, upper limbs state；
Position system unit, it is connected with the main control unit, by the real-time position information of vehicle, continuously drives distance letter
Cease, continuously drive temporal information and send to main control unit, and by main control unit judge driver whether fatigue driving；
Alarm Unit, the Alarm Unit are connected with the main control unit, and when main control unit, to judge that driver has dangerous
During driving behavior, the Alarm Unit carries out sound prompting.
In another embodiment of onboard system based on above-mentioned vehicle security drive early warning, it is characterised in that also include：
Communication unit, be connected with main control unit, for image acquisition units are gathered driver's image information, vehicle shape
State information is uploaded onto the server system, and the image information of driver and operation information, the state of vehicle are preserved by server system
Information, positional information；
Server system, it is connected with communication unit, vehicle is positioned in real time, driver identity is identified, and
Judgement, record, the driving behavior of memory of driving person, the behavior of vehicle traveling, car status information.
In another embodiment of onboard system based on above-mentioned vehicle security drive early warning, the main control unit includes
CNN deep learning frame model training modules, the CNN deep learnings frame model training module are schemed according to the driver of reception
As information, re -training CNN deep learning frame models, to improve object recognition rate.
In another embodiment of onboard system based on above-mentioned vehicle security drive early warning, the main control unit includes
RK1108 chips, the RK1108 chips are automatic according to the CNN deep learning frame models of the training module re -training of reception
Upgrading, with the accuracy rate and adaptability of boosting algorithm.
Compared with prior art, the present invention has advantages below：
The vehicle security drive method for early warning provided based on the above embodiment of the present invention and vehicle-mounted early warning system, pass through CNN
Driver's face-image of collection is compared deep learning frame model, judges the driving safety state of driver, and energy
High according to the image of collection, re -training deep learning frame model, algorithm operational efficiency, accuracy rate is high, hardware configuration requirement
Low, cost reduces, practical, obtains the positional information and driving parameters of vehicle, realization pair in real time by position system unit
The behavior management of driving of driver, the management of vehicle traveling, anti-lost car management.
Below by drawings and examples, technical scheme is described in further detail.
Brief description of the drawings
The accompanying drawing of a part for constitution instruction describes embodiments of the invention, and is used to explain together with description
The principle of the present invention.
Referring to the drawings, according to following detailed description, the present invention can be more clearly understood, wherein：
Fig. 1 is the structural representation of one embodiment of the onboard system of vehicle security drive early warning of the present invention.
Fig. 2 is the flow chart of one embodiment of vehicle security drive method for early warning of the present invention.
Fig. 3 is the flow chart of another embodiment of vehicle security drive method for early warning of the present invention.
Fig. 4 is the flow chart of another embodiment of vehicle security drive method for early warning of the present invention.
In figure：1 main control unit, 2 image acquisition units, 3 position system units, 4 Alarm Units, 5 communication units, 6 services
Device system.
Embodiment
The various exemplary embodiments of the present invention are described in detail now with reference to accompanying drawing.It should be noted that：Unless have in addition
Body illustrates that the unlimited system of part and the positioned opposite of step, numerical expression and the numerical value otherwise illustrated in these embodiments is originally
The scope of invention.
Simultaneously, it should be appreciated that for the ease of description, the size of the various pieces shown in accompanying drawing is not according to reality
Proportionate relationship draw.
The description only actually at least one exemplary embodiment is illustrative to be never used as to the present invention below
And its application or any restrictions that use.
It may be not discussed in detail for technology and equipment known to person of ordinary skill in the relevant, but in appropriate situation
Under, the technology and equipment should be considered as part for specification.
It should be noted that：Similar label and letter represents similar terms in following accompanying drawing, therefore, once a certain Xiang Yi
It is defined, then it need not be further discussed in subsequent accompanying drawing in individual accompanying drawing.
Fig. 1 is the structural representation of one embodiment of the onboard system of vehicle security drive early warning of the present invention, such as Fig. 1 institutes
Show, the onboard system of the vehicle security drive early warning of the embodiment includes：
Main control unit 1, the main control unit 1 are used to load CNN deep learning frame models, and the master control 1 includes detection
Algoritic module, driver's image of collection is compared with CNN deep learning frame models by detection algorithm, judges driver's
Driving condition；The actual complex environment that CNN deep learning frame models drive simulating person drives, machine is set to learn face spy automatically
Sign, analyzes and identifies that the face characteristic of driver's face characteristic in driving procedure and Machine self-learning compares, judge driver
Current driving condition；The CNN deep learnings frame model of the present invention can quickly be run on RK1108 chips, in guarantor
Under the precise manner of face detection, the parameter of model is reduced as far as possible.Main control unit 1 includes algoritic module, by carrying out detection calculation
Method, the driver's image and CNN deep learning frame models of collection are compared, judge the driving condition of driver, the detection
Algorithm includes face recognition detection, eye recognition detection and Activity recognition detection；
Image acquisition units 2, described image collecting unit 2 are connected with the main control unit 1, and the realtime graphic of collection is sent out
Deliver to main control unit 1, image acquisition units 2 gather the face face-image of driver in real time, and analyze driver's eyes state,
Headwork, upper limbs state；Image acquisition units 2 are made up of high-definition camera equipment and image processing equipment, pass through high-definition camera
Equipment gathers the high-definition image information of driver in real time, and by the eyes portion of image processing equipment auto-focusing facial image
Position, detect eyes in real time and open closed state, open and close duration, open and close frequency, detect the tilting of human body head, downward shift,
Pitch status information；Detect the positional information of human upper limb；
Position system unit 3, it is connected with the main control unit 1, by the real-time position information of vehicle, continuously drives distance letter
Cease, continuously drive temporal information and send to main control unit 1, and by main control unit 1 judge driver whether fatigue driving, it is described fixed
Position system unit 3 includes GPS positioning system and/or BEI-DOU position system, the position of the real-time registration of vehicle of the energy of position system unit 3
Information, continuously driving speed, continuously drive the time and continuously driving distance for vehicle is calculated, limited continuously by setting driver
Driving time threshold value, when position system unit 3 detects that the continuous driving time of driver exceedes setting time threshold value, master control list
Member 1 sends warning information；
Alarm Unit 4, the Alarm Unit 4 are connected with the main control unit 1, when main control unit 1 judges that driver has not
During safe driving behavior, the Alarm Unit 4 carries out sound prompting.
Communication unit 5, be connected with main control unit 1, for image acquisition units are gathered driver's image information, vehicle
Status information uploads onto the server system 6, by server system 6 preserve the image information of driver and the operation information of vehicle,
Status information, positional information, in a specific embodiment, communication unit 5 is 4G and/or 3G communication modules, when user inserts
When entering 4G and/or 3G mobile card and opening data, services, communication unit 5 provides data communication facility, if being fitted without mobile phone
Card, then do not provide data, services, and system is run according to not networked mode；In another specific embodiment, communication unit 5 is adopted
With satellite communication module, the data transmission channel resource of satellite communication is relied on, there is provided remote data communication；
Server system 7, it is connected with communication unit 5, vehicle is positioned in real time, driver identity is identified,
And judge, record, the driving behavior of memory of driving person, the behavior of vehicle traveling, car status information, server system 7 is to provide
Software upgrading, the platform of public data services, and can be driver's image, vehicle running orbit, the dangerous traveling letter of vehicle
Breath etc. is backed up.
The main control unit 1 includes CNN deep learning frame model training modules, the CNN deep learnings frame model
Training module is according to driver's image information of reception, re -training CNN deep learning frame models, to improve target identification
Rate.The main control unit 1 includes RK1108 chips.
Fig. 2 is the flow chart of one embodiment of vehicle security drive method for early warning of the present invention, as shown in Fig. 2 the vapour
Car safe driving method for early warning includes：
10, CNN deep learning frame models are built, the complex environment and driver-operated state that drive simulating person drives
Index；
20, the contraction CNN methods in CNN deep learning frame models, to driver's Face datection, judge driver
Face state in driving procedure；
30, driver tired driving behavior is detected according to CNN deep learnings frame model, the fatigue driving row
To open closed state for driver's eyes, opening and close frequency, eye-closing period behavior；
40, driver safety driving behavior is detected according to CNN deep learnings frame model, the safe driving row
For be driver in driving procedure transfer visual direction, both hands departure direction disk behavior；
50, the temporal information or range information that real-time collection vehicle continuously drives, when more than setting continuously drive the time or
Continuously drive apart from when, send warning information；
60, the positional information and running orbit of automobile are monitored, and timing identifies driver identity information, and driver is driven
Behavior and driver identity are managed.
Also include：
70, according to driver's driving condition image of collection, re -training CNN deep learning frame models.
It is described that driver's Face datection is included：Detect the face monochrome information of the driver, information that tilts, inclined up and down
Move information and pitch information.
Fig. 3 is the flow chart of another embodiment of vehicle security drive method for early warning of the present invention, as shown in figure 3, described
Carrying out detection to driver tired driving behavior according to CNN deep learnings frame model includes：
101, continuously the eyes of analysis driver open closed state information in the range of setting time；
102, the eyes for calculating driver are opened and close frequency and continuous eye-closing period；
103, judge whether that the eyes that driver occurs are opened and close frequency reduction, and continuous eye-closing period exceedes setting time threshold
Value；
104, if it is, judging driver for fatigue driving, Alarm Unit 4 carries out voice message.
In a specific embodiment, setting time threshold value is 1 second, then CNN deep learnings frame model detects driving
Member in driving procedure continuous closed-eye time more than 1 second, then it is assumed that driver is in fatigue driving state, triggering Alarm Unit 4
Send alarm signal.
Fig. 4 is the flow chart of another embodiment of vehicle security drive method for early warning of the present invention, as shown in figure 4, described
Carrying out detection to driver safety driving behavior according to CNN deep learnings frame model includes：
201, continuously whether the driving condition of detection driver is unsafe driving state in the range of setting time, described
Unsafe driving state includes：Driver receives calls, looks around, drives in driver's driving procedure in driver's driving procedure
Talked in the person's of sailing driving procedure in both hands departure direction disk, driver's driving procedure with other people；
202, judge whether the duration of driver's unsafe driving behavior exceedes setting time threshold value or occur not
Whether the number of safe driving behavior exceedes setting frequency threshold value；
203, if it is, judging that driver drives vehicle for non-secure states, Alarm Unit 4 carries out voice message.
In a specific embodiment, the time threshold set as 1 second, the frequency threshold value set as 2 this, if drive
The duration of member's unsafe driving behavior was more than 1 second, or the number of driver's generation unsafe driving behavior is more than 2 times,
Then judge that driver carries out non-security driving, trigger the alarm of Alarm Unit 4.
During driver drives a car, by opening position system unit 3, real time data can be gathered, if
Driver's continuous running time exceedes N number of hour of setting, then triggers Alarm Unit 4 and remind user that lasting driving time can not surpass
N hours are spent, please park rest；
Judged according to the information of position system unit 3 and time, if traveling continues to exceed N kms, trigger Alarm Unit 4
User is reminded persistently to travel no more than N hours, please park rest.
As product comes into operation, the species of algorithm model is more and more, therefore, according to CNN deep learning frame models
Self-learning algorithm, model re-starts training, is then upgraded to automatically on chip, the accuracy rate and adaptability of boosting algorithm.
Each embodiment is described by the way of progressive in this specification, what each embodiment stressed be and its
The difference of its embodiment, same or analogous part cross-reference between each embodiment.
Description of the invention provides for the sake of example and description, and is not exhaustively or by the present invention
It is limited to disclosed form.Many modifications and variations are obvious for the ordinary skill in the art.Select and retouch
State embodiment and be to more preferably illustrate the principle and practical application of the present invention, and one of ordinary skill in the art is managed
The present invention is solved so as to design the various embodiments with various modifications suitable for special-purpose.
Claims (9)
- A kind of 1. vehicle security drive method for early warning, it is characterised in that including：CNN (Convolutional Neural Network, convolutional neural networks) deep learning frame model is built, simulation is driven The complex environment and driver-operated state index that the person of sailing drives；Contraction CNN methods in CNN deep learning frame models, to driver's Face datection, judge driver's drive the cross Face state in journey；Driver tired driving behavior is detected according to CNN deep learnings frame model, the fatigue driving behavior is to drive The person's of sailing eyes open closed state, open and close frequency, eye-closing period behavior；Driver safety driving behavior is detected according to CNN deep learnings frame model, the safe driving behavior is to drive The behavior in transfer visual direction, both hands departure direction disk of the person of sailing in driving procedure；The temporal information or range information that real-time collection vehicle continuously drives, when continuously driving the time more than setting or continuously drive Apart from when, send warning information；Monitor the positional information and running orbit of automobile, and timing identifies driver identity information, to driver's driving behavior and Driver identity is managed.
- 2. vehicle security drive method for early warning according to claim 1, it is characterised in that also include：According to driver's driving condition image of collection, re -training CNN deep learning frame models.
- 3. vehicle security drive method for early warning according to claim 1 or 2, it is characterised in that described to driver's face Detection includes：Detect face monochrome information, the information that tilts, downward shift information and the pitch information of driver.
- 4. vehicle security drive method for early warning according to claim 1 or 2, it is characterised in that described according to CNN depth Habit frame model carries out detection to driver tired driving behavior to be included：Continuously the eyes of analysis driver open closed state information in the range of setting time；The eyes of calculating driver, which are opened, closes frequency and continuous eye-closing period；Judge whether that the eyes that driver occurs are opened and close frequency reduction, and continuous eye-closing period exceedes setting time threshold value；If it is, judging driver for fatigue driving, warning system carries out voice message.
- 5. vehicle security drive method for early warning according to claim 1 or 2, it is characterised in that described according to CNN depth Habit frame model carries out detection to driver safety driving behavior to be included：Continuously whether the driving condition of detection driver is unsafe driving state in the range of setting time, described dangerous to drive The state of sailing includes：Driver receives calls, looked around in driver's driving procedure in driver's driving procedure, driver drives During talked with other people in both hands departure direction disk, driver's driving procedure；Judge whether the duration of driver's unsafe driving behavior exceedes setting time threshold value or unsafe driving occurs Whether the number of behavior exceedes setting frequency threshold value；If it is, judging that driver drives vehicle for non-secure states, warning system carries out voice message.
- A kind of 6. onboard system of vehicle security drive early warning, it is characterised in that including：Main control unit, the main control unit are used to load CNN deep learning frame models, and the main control unit includes detection algorithm Module, detection algorithm is taken, the driver's image and CNN deep learning frame models of collection are compared, judge driving for driver State is sailed, the detection algorithm includes face recognition detection, eye recognition detection and Activity recognition detection；Image acquisition units, described image collecting unit are connected with the main control unit, and the realtime graphic of collection is sent to master Control unit, image acquisition units gather the face face-image of driver in real time, and analyze driver's eyes state, head is moved Make, upper limbs state；Position system unit, it is connected with the main control unit, by the real-time position information of vehicle, continuously drives range information, company Continuous travel-time information is sent to main control unit, and by main control unit judge driver whether fatigue driving；Alarm Unit, the Alarm Unit are connected with the main control unit, when main control unit judges that driver has unsafe driving During behavior, the Alarm Unit carries out sound prompting.
- 7. the onboard system of vehicle security drive early warning according to claim 6, it is characterised in that also include：Communication unit, be connected with main control unit, for image acquisition units are gathered driver's image information, vehicle-state letter Breath uploads onto the server system, by server system preserve the image information of driver and the operation information of vehicle, status information, Positional information；Server system, it is connected with communication unit, vehicle is positioned in real time, driver identity is identified, and judge, Record, the driving behavior of memory of driving person, the behavior of vehicle traveling, car status information.
- 8. the onboard system of vehicle security drive early warning according to claim 6, it is characterised in that the main control unit bag Include CNN deep learning frame model training modules, the CNN deep learnings frame model training module is according to the driver of reception Image information, re -training CNN deep learning frame models, to improve object recognition rate.
- 9. the onboard system of vehicle security drive early warning according to claim 6, it is characterised in that the main control unit bag Include RK1108 chips.
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