CN201337458Y - Real-time monitoring device for fatigue state of driver - Google Patents
Real-time monitoring device for fatigue state of driver Download PDFInfo
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- CN201337458Y CN201337458Y CNU2009201012937U CN200920101293U CN201337458Y CN 201337458 Y CN201337458 Y CN 201337458Y CN U2009201012937 U CNU2009201012937 U CN U2009201012937U CN 200920101293 U CN200920101293 U CN 200920101293U CN 201337458 Y CN201337458 Y CN 201337458Y
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Abstract
The utility model discloses a real-time monitoring device for the fatigue stage of the driver, which is mainly used for real-time monitoring the fatigue state of the vehicle drivers in the driving process, and comprises a data collector, a video decoder and an image processing system. The real-time monitoring device is characterized in that the output end of the data collector is connected with the input end of the video decoder; the output end of the vide decoder is connected with the input end of the image processing system; and the output end of the image processing system is connected with an alarm and a data reading output port which is a 100M network interface. In the utility model, a CCD camera of an infrared filter is arranged for acquiring the video image signal of the driver face, the video signal is converted into the digital signal meeting with the standards through the video decoder, and the driver can be judged whether in the fatigue state by a memory and a media digital processor according to the comprehensive parameters of the eyes blinking frequency and the closing time so as to perform alarming and indication timely; meanwhile, the image is stored when alarming, and the process traceability can be carried out when necessary.
Description
Technical field
The present invention relates to a kind of personnel's fatigue state real-time monitoring device, be mainly used in the fatigue state of vehicle drive personnel in driving procedure monitored in real time.
Background technology
It is a major reason that causes pernicious vehicle accident that tired driver is driven, because the vehicle accident that fatigue driving caused, still is shared ratio at absolute number no matter, all is very high.Therefore, develop and a kind ofly can monitor driver's fatigue state and the device of reporting to the police effectively, in real time, just seem is of practical significance and necessity very much.Lot of domestic and international units and individual actively researching and developing the real time monitoring of driving at tired driver, mainly contain following several.
The frequent downward-sloping principle of head meeting when one, the people being arranged according to most people's fatigue, applied for pressing " driving fatigue alarm set " (patent No.: utility model patent 200620132320.3) of head state-detection fatigue, this device is installed in the driver's chair occipitalia, adopt infrared ray sensor to measure the variation of driver's head position, if the incident of leaving or being offset then in time starts and reports to the police.But because driver's individual variation is very big, some people head state when tired, sleepy can not change, in addition, and loose medicated cap; Jewelry etc. all can influence monitoring result on the head, and therefore, this technology has significant limitation.
Two, there is producer to research and develop the tired early warning system of Wrist watch type (the tired early warning system of TWS car steering),, judges the fatigue state of human body by the sensor measurement body temperature of being close to wrist, the variation of pulse.Sound, vibration etc. stimulates human body, reminds the driver to stop to drive.This technology existence is subject to the anthropic factor influence, as: the tightness of wearing; Human body is fat or thin; Perspire and shell temperature etc. all can influence the defective of measurement result.
Three, present, released the glasses type fatigue detection device on the market.The time once nictation was far longer than principle just often when this alarm utilized the people sleepy, reported to the police by the infrared facility monitoring frequency of wink triggering of glasses inboard, reminded the driver to avoid fatigue driving accidents caused.This technology just seems inconvenient for the driver who wears glasses for the myopia and custom wear dark glasses.
The invention detailed content
Need defectives such as wearable sensors and human body differentia influence monitoring result in order to overcome existing fatigue monitoring technology driver, this utility model provides a kind of personnel's fatigue state real-time monitoring device.This device adopts non-intervening mode to need not the driver and wears any pick off, be not subjected to the influence of driver's individual variation, driving habits, can realize human pilot at different vehicle driving postures, different illuminating position and wear glasses, the fatigue state under the situation such as sunglasses monitors in real time, and carry out the audio alert prompting according to the degree of fatigue of human pilot, image in the time of will reporting to the police is simultaneously stored, and can the process of carrying out relate in case of necessity.
This utility model is achieved by following technical proposals: comprise data acquisition unit, Video Decoder, image processing system, the outfan of data acquisition unit connects the input of Video Decoder, the outfan of Video Decoder connects the input of image processing system, and the outfan of image processing system connects alarm and the data read delivery outlet is the 100M network interface; Image processing system is made of Flame Image Process accelerator, alarm image memorizer, 4M*16BIT image storage, program storage, digital media processor, the Flame Image Process accelerator connects alarm image memorizer, 4M*16BIT image storage and digital media processor, the outfan of Flame Image Process accelerator connects alarm, digital media processor linker memorizer, digital media processor are output as data output-100M network interface.
Described data acquisition unit uses initiatively light source of one group of infrared ray; A CCD infrared pick-up head and an infrared ray filter plate are formed driver's face feature image capturing system;
Infrared ray initiatively light source throws light on to driver's face, and infrared filter is installed on the camera lens of CCD infrared pick-up head, and greatly degree has reduced the interference of extraneous light, and has eliminated the influence of sunglasses; The monitoring of use non-intervention type, to the normal driving of human pilot without any interference, the CCD photographic head obtains the driver in real time through the filtered face of infrared filter state video signal, this video signal is converted to standard compliant digital video signal by Video Decoder, by Flame Image Process, condition discrimination system blink dynamic frequency and the comprehensive parameters of eyes closed time according to eyes, judge whether the driver is in fatigue state, and in good time alarm, image in the time of will reporting to the police is simultaneously stored, and can the process of carrying out relate in case of necessity.
The image of the some width of cloth eyes of described 4M*16BIT image memory stores is as the template of eye location.
Described Flame Image Process accelerator adopts eye location: intra-frame trunk, masterplate coupling and infrared ray form reflection ray on pupil comprehensive distinguishing method positions, and accurate positioning, processing speed are fast.
Use is based on the image transformation algorithm of pseudo-colours, to eliminate the influence of different exposure intensities.
Adopt Huo Si doffer (Hausdorff) distance algorithm, so that different facial pose, orientation and yardstick are monitored.
Use neutral net to differentiate algorithm,, differentiate and whether locate fatigue state according to state (opening still closed) and the time of this state continuance and the frequency of blinking of eyes.
The utlity model has following advantage:
1, adopts the monitoring method of non-intervention, be not subjected to the influence of driver's individual variation, driving habits, vehicle driving posture, also do not disturb driver's normal driving.
2, adopt the method that infrared ray initiatively throws light on and infrared ray filters, can effectively eliminate extraneous various light and disturb, effectively eliminate sunglasses and disturb, and can any interference not arranged human pilot.Good background inhibitory action is arranged simultaneously, filtering enter most of ambient light of ccd video camera, simultaneously also filtering most background image, reduced the interference of external environment, reduced the complexity of Flame Image Process.
3, the reflection ray that adopts intra-frame trunk, masterplate coupling and infrared ray to form on pupil positions eyes, improves processing speed; Use is based on the image transformation algorithm of pseudo-colours, to eliminate the influence of different exposure intensities; Adopt Hausdorff (Huo Si doffer) distance algorithm of revising, can monitor different facial pose, orientation and yardstick; Use neutral net to differentiate algorithm, have good real-time.
4, adopt the way of principal and subordinate processor parallel processing to constitute real-time embedding system, finish works of treatment such as image transformation, human eye state identification by coprocessor, the function that primary processor is finished human eye identification, cut apart, the hardware and software parallel processing.
Description of drawings:
Fig. 1 is the block diagram of this utility model embodiment.
Fig. 2 is that the fatigue state of this utility model embodiment is differentiated flow chart.
Fig. 3, Fig. 4 are the electrical schematic diagrams of digital media processor among this utility model embodiment, and wherein Fig. 3 is the power pin of digital media processor TMS320DM642 module, Fig. 4 be digital media processor TMS320DM642 module interface.
Fig. 5, Fig. 6, Fig. 7, Fig. 8 are the electrical schematic diagrams of Video Decoder among this utility model embodiment, and wherein Fig. 5 is a Video Decoder ADV7189B module, and Fig. 6 is a static memory, and Fig. 7 is the coupling capacitance loop, and Fig. 8 is the NAND gate flash memory module.
Fig. 9 is a supply unit module among this utility model embodiment.
Figure 10 is the electrical schematic diagram of phonetic alarm among this utility model embodiment.
Figure 11 is the electrical schematic diagram of program storage among this utility model embodiment.
Figure 12 is the graphical analysis working cell module of data acquisition unit and Flame Image Process accelerator among this utility model embodiment.
Figure 13 is the electrical schematic diagram of alarm image memorizer among this utility model embodiment.
Figure 14 is the electrical schematic diagram of data output interface among this utility model embodiment.
Figure 15 is the circuit diagram of 4M*16BIT image storage among this utility model embodiment.
Figure 16 is the reference diagram of this utility model embodiment circuit board.
Among the figure, 1. data acquisition unit 2. Video Decoder ADV7189B 3. image processing systems 4. phonetic alarm APR9600 5. Flame Image Process accelerator XC3S1000 6.4M*16BIT image storage CY7C1061 7. alarm image memorizer HY27UH08 8. digital media processor TMS320DM642 9. program storage AM29LV033 10. infrared filters 11. infrared light sources 12. infrared C CD video camera 11.100M network interfaces
The specific embodiment
Below by embodiment, and in conjunction with the accompanying drawings this utility model is further described.
Embodiment: shown in Figure 1, comprise data acquisition unit 1, Video Decoder 2, image processing system 3, the outfan of data acquisition unit 1 connects the input of Video Decoder 2, the outfan of Video Decoder 2 connects the input of image processing system 3, and the outfan of image processing system 3 connects alarm 7 and the data read delivery outlet is a 100M network interface 11; Image processing system 3 is made of Flame Image Process accelerator 5, alarm image memorizer 7,4M*16BIT image storage 6, program storage 9, digital media processor 8, Flame Image Process accelerator 5 connects alarm image memorizer 7 and 4M*16BIT image storage 6 and digital media processor 8, the outfan of Flame Image Process accelerator 5 connects alarm 7, digital media processor 8 linker memorizeies 9, digital media processor 8 is output as data output-100M network interface.
Described data acquisition unit uses initiatively light source 11 of one group of infrared ray; A CCD infrared camera 12 and an infrared ray filter plate 10 are formed driver's face feature image capturing system.
In the present embodiment, infrared filter centre wavelength 940nm, half-wave bandwidth 20nm; Infrared light sources centre wavelength 940nm; Infrared C CD video camera is formed driver's face feature acquisition system, the full tv composite signal of ccd video camera output is converted to standard compliant digital video signal through Video Decoder, export the Flame Image Process accelerator to and carry out pretreatment, read in image storage by digital media processor then and carry out human eye identification, after cutting apart, judge eye state (whether tired) with neutral net, locate fatigue driving state when detecting the driver, by the phonetic alarm signal that gives the alarm, simultaneously, should deposit the alarm image memorizer in by the period image, can read by the 100-M network Ethernet in the system in case of necessity, the process of carrying out is related.
Differentiation workflow shown in Figure 2 is carried out eye location, and condition discrimination has been realized aforementioned goal of the invention effectively.
The fatigue state of present embodiment is differentiated workflow:
1, at first each variable is carried out initialization, reading video data then.At first the reflection ray that forms at pupil according to infrared ray is to eye location, if the location failure is if first frame directly enters the eyes matching process and seeks eyes, seek eyes otherwise carry out intra-frame trunk earlier, carry out the eyes coupling after the intra-frame trunk failure again and seek.
2, in order to improve the speed of service, set stripper (maskFlage) variable, the former frame template that the match is successful is carried out record, present frame with which template mates earlier according to the value decision of this variable.
3, eyes couplings is to carry out between the image behind the pseudocolor transformation, promptly earlier present frame is carried out pseudocolor transformation, uses the eyes template image behind the pseudocolor transformation to mate then.
4, note also not counting in the practical programs.
5, the process of eyes template matching is as follows: extract image segments behind the present frame pseudocolor transformation one by one according to template size, then with the eyes template matching behind this fragment and the pseudocolor transformation, use function Distance2 to calculate matching distance, the more little coupling of distance is good more.4 minimum image segments of distance are selected, and judge three kinds of states of eyes by function is_eye, if eyes, write down the upper left corner coordinate Y=[left of current eyes; Top], wait until intra-frame trunk and use; And the maskFlage variable is set to when front template number, waits until next frame and uses.
6, the explanation of function Distance2: (imData, modelData) matching distance of calculating two width of cloth image imData and modelData is returned matching distance HD to function f unction HD=Distance2.At first computed image one is to the distance H D1 of image two for this function, and computed image two is to the distance H D2 of image one then, and the maximum in the two is exactly return of value HD.HD1 is calculated as follows: to each point in the image one, in image two, seek the point that equates with its pixel value, and in the computed image two these in the image one this apart from distancel, these minimum value and value are called direct range directHD1, and the meansigma methods of direct range is exactly the HD1 of requirement.The computational methods of HD2 are analogized.
7, the explanation of function is_eye: function f unction eyeFlag=is_eye (imageData) judges the state of image imageData, here image imageData tentatively is judged as eye image through matching process, return of value eyeFlag gets 1 ,-1,0 respectively, and respectively corresponding eyes " are opened ", eyes " close " and be not eyes.This is the classification function of 3 outputs, output valve X2 be one by three elementary composition vectors, first element equals-1 correspondence and not to be eyes, and second element equals-1 correspondence eyes and " close ", and the 3rd element equals-1 correspondence and eyes and " open ".Notice that use therein non-linear classification function is hyperbolic tangent function tanh.
8, function f unction[Xk, p]=(explanation p): this is an intra-frame trunk computing function to myKK for Xk, Yk, and the true coordinate, the p that wherein import Xk and be former frame prediction eye position coordinate, Yk and be the present frame eyes are the covariance matrix of corresponding Xk.Output Xk is the predicted current frame eyes coordinates, and p is the covariance matrix of the corresponding Xk of predicted current frame.Specifying needs much formula about adding up, and you can programme in the same old way, notice that wherein function inv representing matrix is inverted.
9, the explanation of function f unction transImdata=transform2 (origImdata): this function carries out the pseudocolor transformation of image origImdata, add up 8 of each pixel exactly and face thresholding, be higher than this pixel value counting 0, equal to count 1, less than counting 2, these 8 numerals are formed 8 trits.
10, the explanation of function f unction Cdata=pseudoRGB2 (transImdata): 8 trits that above-mentioned functional transformation is obtained are transformed to 10 systems and obtain gray level image, are used for images match.
Claims (3)
1, personnel's fatigue state real-time monitoring device, comprise data acquisition unit, Video Decoder, image processing system, it is characterized in that the outfan of data acquisition unit connects the input of Video Decoder, the outfan of Video Decoder connects the input of image processing system, and the outfan of image processing system connects alarm and the data read delivery outlet is the 100M network interface.
2, personnel's fatigue state real-time monitoring device according to claim 1, it is characterized in that image processing system is made of Flame Image Process accelerator, alarm image memorizer, 4M*16BIT image storage, program storage, digital media processor, the Flame Image Process accelerator connects alarm image memorizer, 4M*16BIT image storage and digital media processor, the outfan of Flame Image Process accelerator connects alarm, digital media processor linker memorizer, digital media processor are output as data output-100M network interface.
3, personnel's fatigue state real-time monitoring device according to claim 1 is characterized in that described data acquisition unit uses initiatively light source of one group of infrared ray; A CCD infrared pick-up head and an infrared ray filter plate are formed driver's face feature image capturing system.
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Cited By (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102393989A (en) * | 2011-07-28 | 2012-03-28 | 山西智济电子科技有限公司 | Real-time monitoring system of driver working state |
| CN103347446A (en) * | 2010-12-10 | 2013-10-09 | Tk控股公司 | System for monitoring a vehicle driver |
| CN104464193A (en) * | 2014-12-12 | 2015-03-25 | 清华大学苏州汽车研究院（吴江） | Control box of driver fatigue detection early-warning system |
| CN104574819A (en) * | 2015-01-09 | 2015-04-29 | 安徽清新互联信息科技有限公司 | Fatigued drive detection method based on mouth features |
| CN104590130A (en) * | 2015-01-06 | 2015-05-06 | 上海交通大学 | Adaptive Adjustment Method of Rearview Mirror Based on Image Recognition |
| CN107818569A (en) * | 2017-11-09 | 2018-03-20 | 王涛 | A kind of visual impairment monitoring alarm method |
| CN108283491A (en) * | 2018-01-24 | 2018-07-17 | 京东方科技集团股份有限公司 | Display panel and display device |
| CN109002817A (en) * | 2018-08-31 | 2018-12-14 | 武汉理工大学 | Adapter tube performance monitoring early warning system based on automatic driving vehicle driving fatigue temporal behavior |
| CN110163037A (en) * | 2018-03-14 | 2019-08-23 | 北京航空航天大学 | Monitor method, equipment, system, processor and the storage medium of driver status |
| CN110786869A (en) * | 2019-10-29 | 2020-02-14 | 浙江工业大学 | A method for detecting the fatigue level of programmers |
| CN110837819A (en) * | 2019-11-21 | 2020-02-25 | 北京健康有益科技有限公司 | Fatigue degree detecting system based on artificial intelligence |
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- 2009-01-10 CN CNU2009201012937U patent/CN201337458Y/en not_active Expired - Lifetime
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| CN103347446A (en) * | 2010-12-10 | 2013-10-09 | Tk控股公司 | System for monitoring a vehicle driver |
| CN103347446B (en) * | 2010-12-10 | 2016-10-26 | Tk控股公司 | For monitoring the system of vehicle driver |
| CN102393989A (en) * | 2011-07-28 | 2012-03-28 | 山西智济电子科技有限公司 | Real-time monitoring system of driver working state |
| CN102393989B (en) * | 2011-07-28 | 2013-03-27 | 山西智济电子科技有限公司 | Real-time monitoring system of driver working state |
| CN104464193A (en) * | 2014-12-12 | 2015-03-25 | 清华大学苏州汽车研究院（吴江） | Control box of driver fatigue detection early-warning system |
| CN104464193B (en) * | 2014-12-12 | 2017-08-25 | 清华大学苏州汽车研究院（吴江） | A kind of driver fatigue detects the control box of early warning system |
| CN104590130A (en) * | 2015-01-06 | 2015-05-06 | 上海交通大学 | Adaptive Adjustment Method of Rearview Mirror Based on Image Recognition |
| CN104574819A (en) * | 2015-01-09 | 2015-04-29 | 安徽清新互联信息科技有限公司 | Fatigued drive detection method based on mouth features |
| CN107818569A (en) * | 2017-11-09 | 2018-03-20 | 王涛 | A kind of visual impairment monitoring alarm method |
| CN108283491A (en) * | 2018-01-24 | 2018-07-17 | 京东方科技集团股份有限公司 | Display panel and display device |
| CN108283491B (en) * | 2018-01-24 | 2021-08-17 | 京东方科技集团股份有限公司 | Display panels and display devices |
| CN110163037A (en) * | 2018-03-14 | 2019-08-23 | 北京航空航天大学 | Monitor method, equipment, system, processor and the storage medium of driver status |
| CN109002817A (en) * | 2018-08-31 | 2018-12-14 | 武汉理工大学 | Adapter tube performance monitoring early warning system based on automatic driving vehicle driving fatigue temporal behavior |
| CN110786869A (en) * | 2019-10-29 | 2020-02-14 | 浙江工业大学 | A method for detecting the fatigue level of programmers |
| CN110786869B (en) * | 2019-10-29 | 2021-12-21 | 浙江工业大学 | Method for detecting fatigue degree of programmer |
| CN110837819A (en) * | 2019-11-21 | 2020-02-25 | 北京健康有益科技有限公司 | Fatigue degree detecting system based on artificial intelligence |
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Denomination of utility model: Real-time monitoring device for fatigue state of driver Effective date of registration: 20131121 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2013990000879 |
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Denomination of utility model: Real-time monitoring device for fatigue state of driver Effective date of registration: 20141222 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2014990001107 |
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Denomination of utility model: Real-time monitoring device for fatigue state of driver Effective date of registration: 20160226 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2016140000002 |
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Denomination of utility model: Real-time monitoring device for fatigue state of driver Effective date of registration: 20170301 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2017140000003 |
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Date of cancellation: 20180321 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2017140000003 |
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Denomination of utility model: Real-time monitoring device for fatigue state of driver Effective date of registration: 20180412 Granted publication date: 20091104 Pledgee: Pudong Shanghai Development Bank Limited by Share Ltd Taiyuan branch Pledgor: Shanxi Zhiji Electronic Technology Co., Ltd. Registration number: 2018140000005 |
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