CN104574817A - Machine vision-based fatigue driving pre-warning system suitable for smart phone - Google Patents
Machine vision-based fatigue driving pre-warning system suitable for smart phone Download PDFInfo
- Publication number
- CN104574817A CN104574817A CN201410816357.7A CN201410816357A CN104574817A CN 104574817 A CN104574817 A CN 104574817A CN 201410816357 A CN201410816357 A CN 201410816357A CN 104574817 A CN104574817 A CN 104574817A
- Authority
- CN
- China
- Prior art keywords
- driver
- fatigue
- eye
- information
- machine vision
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
-
- 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
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Theoretical Computer Science (AREA)
- Business, Economics & Management (AREA)
- Emergency Management (AREA)
- Measurement Of The Respiration, Hearing Ability, Form, And Blood Characteristics Of Living Organisms (AREA)
- Traffic Control Systems (AREA)
Abstract
The invention discloses a machine vision-based fatigue driving pre-warning system suitable for a smart phone. The machine vision-based fatigue driving warning system comprises the smart phone which acquires the facial information of a driver through a camera and acquires the speed information, the acceleration information and the gravitational acceleration information of a current vehicle through a sensor information acquisition module, a fatigue detection module which judges the fatigue state of the driver by detecting an eye movement status and the change of head posture of the driver, a driver behavior analysis module which is used for modeling the driving status information of a user comprehensively, and comparing the model of the driving status information with a statistical analysis method model to assist the judgment for the current behavior status of the driver, and a pre-warning module which is used for comparing the fatigue detection result with the driving behavior analysis result comprehensively, and giving a fatigue alarm by a voice prompt. The driving behavior is analyzed automatically to form a data analysis library, and a mobile phone acceleration sensor is combined to judge the dangerous driving behavior of the driver.
Description
Technical field
the present invention relates to machine vision driver fatigue monitor system, be specifically related to a kind of be applicable to smart mobile phone based on machine vision driver fatigue monitor system.
Background technology
along with the quick increase of vehicle guaranteeding organic quantity and the develop rapidly of highway, traffic hazard occurs again and again, causes huge property loss and casualties to countries in the world.How to reduce traffic hazard to have become international a difficult problem, wherein, preventing fatigue driving is the gordian technique that countries in the world and Ge great automobile vendor are doing one's utmost to research and develop at present.Latest data according to Ministry of Communications is added up, annual China because traffic safety accident casualty number is more than 200,000 people, wherein 50% come from driver's unconsciousness wake up thus lead to traffic accident.Long-duration driving, do not have enough sleep, the factor such as physiological change is the main cause causing fatigue driving to produce.During fatigue driving, driver can feel absent-minded, absent minded, blurred vision, judgment decline, and in this case continues to drive, and very easily occurs that operation pauses or revises error, causes traffic hazard to occur.And data analysis display, the zero point to six in morning every day is the time period occurred frequently of fatigue driving, and visible fatigue driving, except arranging reasonable running time, also needs effective scientific and technological prior-warning device, at utmost could reduce the generation of fatigue driving accident.
at present, what be equipped with driver fatigue monitor system in car load enterprise mainly contains the assembly plant such as benz, Volvo, masses, Toyota, BYD, the mode adopted mainly is divided into two kinds, a kind of is the facial characteristics utilizing camera to take driver, and differentiate whether it is in fatigue state by the eye state of driver, its camera major part is positioned over panel board inside, through the space shooting driver of bearing circle.Another kind utilizes the driving behavior of driver (rotational characteristic of such as bearing circle) to judge its fatigue state.But be mainly provided in respective middle-and-high-ranking model vehicle, and the interference of light source to camera lens in various direction cannot be avoided, particularly evening insufficient light, will be more difficult to the identification of face.In rear dress market, also there is the equipment of some preventing fatigue drivings.Mainly contain the product, the overtime assisting automobile driver product that carry product, application driving trace method of discrimination of application ECG detecting method, these products or use on and driver produce Body contact interference drive, or Cleaning Principle can not accurately reflect driver fatigue situation, all effectively can not carry out driver tired driving warning function, acceptance level is commercially general.Through research and market feedback, applied for machines vision technique analyzes the scheme that fatigue driving early warning product is high precision, the degree of recognition is high of driver's facial expression information by camera.But at present price is commercially up to several thousand yuan for this series products, expensive, this is also cause to fail one of major reason of popularizing in Chinese driving fatigue early warning system.If now conventional smart mobile phone can be utilized to realize the object of giving fatigue pre-warning, this is convenient in the universal of giving fatigue pre-warning and application.
smart mobile phone of today is generally integrated with camera, acceleration transducer and gravity sensor, has the internal memory of CPU and 512MB of at least 1GHz simultaneously.These hardware conditions can meet designs and Implements a driver fatigue monitor system on smart mobile phone.
Summary of the invention
the traffic hazard problem caused for fatigue driving is day by day serious, and the equipment of current preventing fatigue driving can not well be popularized.The present invention proposes a kind of be applicable to smart mobile phone based on machine vision driver fatigue monitor system, universalness and the portability of fatigue driving warning function can be convenient to, more driver is allowed to obtain fatigue drive prompting in time on the run, for safety of life and property provides safeguard.
technical scheme of the present invention is: a kind of be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, comprise smart mobile phone, by camera collection driver facial information, the velocity information of Current vehicle is gathered, acceleration information and collection acceleration of gravity information by sensor information acquisition module;
fatigue detecting module, by judging the fatigue state of driver in conjunction with the detection of eye motion state and the detection of driver head's attitudes vibration;
driving behavior analysis module, carries out comprehensive modeling to user's driving condition information, contrasts with statistical analysis method model, for auxiliary judgment driver current behavior state;
warning module, for carrying out Comprehensive Correlation to fatigue detection result and driving behavior analysis result, carries out fatigue warning by voice reminder.
further, the detection of described eye motion state is realized by the location of face feature point.
further, the change of described driver head's attitude is by three-dimensional head model inspection.
further, the detection of described eye motion state comprises eye contour extraction and human eye location; Described eye contour extracts and comprises from pilothouse scene image, find human eye position and regional extent; The outline line of palpebra inferior, iris on extracting further within the scope of this; Described human eye location comprises the following steps: set up based on the local texture model from quotient graph;
utilize the cluster that facial regional area is good, set up stacked shape;
the active shape model based on points distribution models is adopted to position, when driving, by realizing the individuation customization of shape parameter to the on-line study of driver's face shape.
further, the change of described driver head's attitude comprises:
angle point in facial zone is followed the tracks of, according to the epipolar line restriction relation between perspective projection model hypograph coordinate, eigenmatrix is resolved based on the displacement on image between unique point, and utilize eigenmatrix to solve rotation matrix and translation matrix, and then calculate the relative change of attitude angle between consecutive frame in video sequence;
based on the three-dimensional reconstruction of Candide model realization driver head, and be similarity measure with mutual information, realized the determination of driver's initial attitude angle by three-dimensional model registration technology;
calculate the absolute pose angle of driver in world coordinate system.
further, the characteristic parameter of described Extract eyes comprise closed-eye time number percent, the longest wink time, frequency of wink, on average open eyes degree, time window length that specific closed-eye time is corresponding, on average open eyes time, maximum eye opening time, the maximal value of average closed-eye time, maximum closed-eye time, closed-eye time and eye opening time ratios, the mean value of closed-eye time and eye opening time ratios, the maximum dwell of iris, iris transverse direction on average translational speed, pupil without rest index, the upper and lower asymmetry of iris.
further, the characteristic parameter that described head pose extracts comprises head steering locking angle, head movement speed, nods, head is towards distribution.
further, described fatigue detecting module searches the value of all conditions probability in discriminant equation respectively according to conditional probability table, and makes successive multiplication, and driving condition corresponding to maximum probability person is the estimated value of the current fatigue state of driver.
advantage of the present invention is:
1. this system gathers driver's facial expression information by mobile phone camera, and the tired algorithm of high robust detects in real time unsafe conditions such as the fatigue of driver and dispersion attention and provides warning message.Take into full account the time-domain stability of driving environment in driving conditions, propose a kind of facial zone partitioning algorithm fully utilizing machine learning, online adaptive skin color modeling, pure background modeling technology, and by tracking to angle point in facial zone, based on epipolar line restriction establishing equation, model is resolved at driver's relative attitude angle.By to this driver behavior automatic analysis, form the Data analysis library for this driver, judge driver's dangerous driving behavior in conjunction with mobile phone acceleration sensor, give the safety guarantee that driver is initiatively intelligent.
2., by designing native system on smart mobile phone, universalness and the portability of fatigue driving warning function can be convenient to, allow more driver obtain fatigue drive prompting in time on the run, for safety of life and property provides safeguard.
Accompanying drawing explanation
below in conjunction with drawings and Examples, the invention will be further described:
fig. 1 is the system structural framework figure of driver fatigue monitor system in the present invention;
fig. 2 is the workflow diagram of driver fatigue monitor system in the present invention;
fig. 3 is Attitude estimation and the pose calibrating process flow diagram of fatigue detecting module in the present invention;
fig. 4 is the extracting method process flow diagram of the eye contour of fatigue detecting module in the present invention;
fig. 5 is the tired method of discrimination process flow diagram of fatigue detecting module in the present invention.
Embodiment
for making the object, technical solutions and advantages of the present invention clearly understand, below in conjunction with embodiment also with reference to accompanying drawing, the present invention is described in more detail.Should be appreciated that, these describe just exemplary, and do not really want to limit the scope of the invention.In addition, in the following description, the description to known features and technology is eliminated, to avoid unnecessarily obscuring concept of the present invention.
embodiment:
below in conjunction with accompanying drawing, preferred embodiment of the present invention is described further.
the present invention, main use carries out fatigue detecting based on the mode of machine vision, is aided with driving behavior analysis and strengthens the accuracy and robustness that detect.
smart mobile phone of today is generally integrated with camera, acceleration transducer, gravity sensor and locating module, has the internal memory of CPU and 512MB of at least 1GHz simultaneously, these hardware conditions.
as shown in Figure 1, in the present invention, be applicable to the driver fatigue monitor system of smart mobile phone, comprise smart mobile phone, fatigue detecting module, driving behavior analysis module and warning module four modules.
smart mobile phone comprises image capture module and sensor information acquisition module.
image capture module: carry out the collection of driver's facial information by camera.
sensor information acquisition module mainly comprises: locating module, gravity sensor, acceleration sensing, the velocity information of Current vehicle is gathered by locating module, acceleration information is gathered by acceleration transducer, acceleration of gravity information is gathered, for driver behavioural analysis by gravity sensor.
warning module: warning message, mainly through voice reminder, carries out fatigue warning.
fatigue detecting module: the fatigue state being judged driver by the fusion of two aspect information: one, realizes the detection of eye motion state by the location of face feature point; Its two, by the change of three-dimensional head model inspection driver head attitude.
driving behavior analysis module: make full use of mobile phone sensor information, carries out comprehensive modeling to user's driving condition information, contrasts with statistical analysis method model, auxiliary judgment driver current behavior state, auxiliary fatigue detecting module.Strengthen accuracy and the adaptivity of systems axiol-ogy.
the workflow of the Vehicle Anti-Theft System that the present invention is based on recognition of face is provided below in conjunction with Fig. 2:
1. giving fatigue pre-warning system starts;
2. camera starts to gather driver's face-image;
3., after image acquisition completes, by machine vision fatigue detecting module, carry out fatigue detecting;
4. sensor information acquisition module, gathers the velocity information, acceleration information, acceleration of gravity information etc. of Current vehicle, and carries out driving behavior analysis by driver behavior model;
5. pair fatigue detection result and driving behavior analysis result carry out Comprehensive Correlation, judge whether to carry out giving fatigue pre-warning.
6. early warning result exports
be illustrated in figure 3 the algorithm flow of head pose estimation.
in Attitude estimation, two steps have been come.The first step, by following the tracks of the angle point in facial zone, according to the epipolar line restriction relation between perspective projection model hypograph coordinate, eigenmatrix is resolved based on the displacement on image between unique point, and utilize eigenmatrix to solve rotation matrix and translation matrix, and then calculate the relative change of attitude angle between consecutive frame in video sequence; Second step, the three-dimensional reconstruction of driver's head based on Candide model realization, and be similarity measure with mutual information, the determination of driver's initial attitude angle is achieved by three-dimensional model registration technology.After the relative change obtaining attitude angle between consecutive frame in initial attitude angle and video sequence, just can calculate the absolute pose angle of driver in world coordinate system.
if Fig. 4 is the explanation of eye contour extracting method.
eye contour extraction algorithm mainly comprises two links, and one is from pilothouse scene image, find human eye position and regional extent; Two is the outline lines extracting palpebra inferior, iris etc. within the scope of this further.In human eye location, this method adopts active shape model (the Active Shape Model based on points distribution models, ASM) algorithm, and for its problem not good to illumination variation, attitudes vibration robustness in actual environment, three aspect designs are carried out to algorithm: first, establish based on the local texture model from quotient graph to overcome the impact of illumination variation; Secondly, make full use of the cluster that facial regional area is good, establish stacked shape, to adapt to the wide-angle deflection of driver's attitude; Again, when driving, by realizing the individuation customization of shape parameter to the on-line study of driver's face shape, the registration for ASM algorithm provides stricter constraint.
fig. 5 is tired discrimination model.
in this method, the feature of Extract eyes comprise closed-eye time number percent, the longest wink time, frequency of wink, on average open eyes degree, time window length that specific closed-eye time is corresponding, on average open eyes time, maximum eye opening time, the maximal value of average closed-eye time, maximum closed-eye time, closed-eye time and eye opening time ratios, the mean value of closed-eye time and eye opening time ratios, the maximum dwell of iris, iris transverse direction on average translational speed, pupil without 15 parameters such as rest index, the upper and lower asymmetry of iris.The feature that head pose extracts comprises head steering locking angle, head movement speed, nods, head is towards distribution 4 parameters.
according to 19 parameter values detected in real time, respectively clear-headed, tired, under major fatigue, search the value of all conditions probability in discriminant equation according to conditional probability table respectively, and make successive multiplication.Three compares, and state corresponding to maximum probability person is the estimated value of the current fatigue state of driver.
native system gathers driver's facial expression information by mobile phone camera, and the tired algorithm of high robust detects in real time unsafe conditions such as the fatigue of driver and dispersion attention and provides warning message.This invention has taken into full account the time-domain stability of driving environment in driving conditions, propose a kind of facial zone partitioning algorithm fully utilizing machine learning, online adaptive skin color modeling, pure background modeling technology, and by tracking to angle point in facial zone, based on epipolar line restriction establishing equation, model is resolved at driver's relative attitude angle.Meanwhile, the individual three-dimensional reconstruction of driver's head based on Candide model realization, and the determination of driver's initial attitude angle is completed by three-dimensional model registration.And the conspicuousness of eye motion parameter differences under adopting statistical method analytic demonstration different fatigue level, establish the fatigue characteristic space based on eye motion feature, and simulate the cognitive process of people, propose and adopt the priori obtained based on training sample to classify to Fatigue pattern at the driving task initial stage, and based on the discrimination method that bayesian belief networks is inferred driver fatigue state on self study basis.Meanwhile, by this driver behavior automatic analysis, form the Data analysis library for this driver, judge driver's dangerous driving behavior in conjunction with mobile phone acceleration sensor, give the safety guarantee that driver is initiatively intelligent.
should be understood that, above-mentioned embodiment of the present invention only for exemplary illustration or explain principle of the present invention, and is not construed as limiting the invention.Therefore, any amendment made when without departing from the spirit and scope of the present invention, equivalent replacement, improvement etc., all should be included within protection scope of the present invention.In addition, claims of the present invention be intended to contain fall into claims scope and border or this scope and border equivalents in whole change and modification.
Claims (8)
1. one kind be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, comprise smart mobile phone, by camera collection driver facial information, the velocity information of Current vehicle is gathered, acceleration information and collection acceleration of gravity information by sensor information acquisition module;
Fatigue detecting module, by judging the fatigue state of driver in conjunction with the detection of eye motion state and the detection of driver head's attitudes vibration;
Driving behavior analysis module, carries out comprehensive modeling to user's driving condition information, contrasts with statistical analysis method model, for auxiliary judgment driver current behavior state;
Warning module, for carrying out Comprehensive Correlation to fatigue detection result and driving behavior analysis result, carries out fatigue warning by voice reminder.
2. according to claim 1 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the detection of described eye motion state is realized by the location of face feature point.
3. according to claim 1 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the change of described driver head's attitude is by three-dimensional head model inspection.
4. according to claim 2 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the detection of described eye motion state comprises eye contour and to extract and human eye is located; Described eye contour extracts and comprises from pilothouse scene image, find human eye position and regional extent; The outline line of palpebra inferior, iris on extracting further within the scope of this; Described human eye location comprises the following steps: set up based on the local texture model from quotient graph;
Utilize the cluster that facial regional area is good, set up stacked shape;
The active shape model based on points distribution models is adopted to position, when driving, by realizing the individuation customization of shape parameter to the on-line study of driver's face shape.
5. according to claim 3 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the change of described driver head's attitude comprises:
Angle point in facial zone is followed the tracks of, according to the epipolar line restriction relation between perspective projection model hypograph coordinate, eigenmatrix is resolved based on the displacement on image between unique point, and utilize eigenmatrix to solve rotation matrix and translation matrix, and then calculate the relative change of attitude angle between consecutive frame in video sequence;
Based on the three-dimensional reconstruction of Candide model realization driver head, and be similarity measure with mutual information, realized the determination of driver's initial attitude angle by three-dimensional model registration technology;
Calculate the absolute pose angle of driver in world coordinate system.
6. according to claim 2 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the characteristic parameter of described Extract eyes comprises closed-eye time number percent, the longest wink time, frequency of wink, average eye opening degree, the time window length that specific closed-eye time is corresponding, the average eye opening time, the maximum eye opening time, average closed-eye time, maximum closed-eye time, the maximal value of closed-eye time and eye opening time ratios, the mean value of closed-eye time and eye opening time ratios, the maximum dwell of iris, iris is average translational speed laterally, pupil is without rest index, the upper and lower asymmetry of iris.
7. according to claim 3 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, the characteristic parameter that described head pose extracts comprises head steering locking angle, head movement speed, nods, head is towards distribution.
8. according to claim 1 be applicable to smart mobile phone based on machine vision driver fatigue monitor system, it is characterized in that, described fatigue detecting module searches the value of all conditions probability in discriminant equation respectively according to conditional probability table, and make successive multiplication, driving condition corresponding to maximum probability person is the estimated value of the current fatigue state of driver.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410816357.7A CN104574817A (en) | 2014-12-25 | 2014-12-25 | Machine vision-based fatigue driving pre-warning system suitable for smart phone |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410816357.7A CN104574817A (en) | 2014-12-25 | 2014-12-25 | Machine vision-based fatigue driving pre-warning system suitable for smart phone |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN104574817A true CN104574817A (en) | 2015-04-29 |
Family
ID=53090766
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410816357.7A Pending CN104574817A (en) | 2014-12-25 | 2014-12-25 | Machine vision-based fatigue driving pre-warning system suitable for smart phone |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104574817A (en) |
Cited By (49)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105034804A (en) * | 2015-05-11 | 2015-11-11 | 南京理工大学 | Vehicle active safety control method and vehicle active safety control system |
| CN105035074A (en) * | 2015-06-23 | 2015-11-11 | 南京理工大学 | Vehicle active safety control method based on portable intelligent equipment |
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| CN105261152A (en) * | 2015-09-30 | 2016-01-20 | 中国民用航空总局第二研究所 | Air traffic controller fatigue detection method based on clustering analysis, device and system |
| CN105303830A (en) * | 2015-09-15 | 2016-02-03 | 成都通甲优博科技有限责任公司 | Driving behavior analysis system and analysis method |
| CN105373767A (en) * | 2015-07-23 | 2016-03-02 | 中山大学深圳研究院 | Eye fatigue detection method for smart phones |
| CN105512613A (en) * | 2015-11-26 | 2016-04-20 | 中山大学 | Smartphone-based eye fatigue detection method |
| CN105718875A (en) * | 2016-01-15 | 2016-06-29 | 广东欧珀移动通信有限公司 | Visual fatigue detection method and user terminal |
| CN106226556A (en) * | 2016-07-06 | 2016-12-14 | 深圳市元征科技股份有限公司 | Acceleration transducer calibration steps and device |
| CN106326912A (en) * | 2015-07-01 | 2017-01-11 | 埃西勒国际通用光学公司 | A user fatigue level analysis component |
| CN106627459A (en) * | 2016-08-30 | 2017-05-10 | 湖北文理学院 | Automobile safety warning and accident warning based on intelligent device and location method |
| CN106710145A (en) * | 2016-12-29 | 2017-05-24 | 清华大学苏州汽车研究院（吴江） | Guided driver tiredness prevention method |
| CN106778532A (en) * | 2016-11-28 | 2017-05-31 | 合肥工业大学 | Based on the driving posture feature sorting technique for removing differentiation size parameter |
| CN107392159A (en) * | 2017-07-27 | 2017-11-24 | 竹间智能科技（上海）有限公司 | A kind of facial focus detecting system and method |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| WO2018014598A1 (en) * | 2016-07-18 | 2018-01-25 | 中兴通讯股份有限公司 | Method and terminal for alerting fatigued driver |
| CN107708553A (en) * | 2015-09-03 | 2018-02-16 | 三菱电机株式会社 | Activity recognition device, air conditioner and robot controller |
| WO2018058275A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Smart driving method and system employing mobile terminal |
| WO2018058273A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Anti-theft method and system |
| WO2018058266A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Mobile terminal-based notification method and system |
| CN108349494A (en) * | 2015-10-31 | 2018-07-31 | 戴姆勒股份公司 | Run the distance of vehicle and the method for speed adjustment function and the driver assistance system for executing this method |
| CN108446600A (en) * | 2018-02-27 | 2018-08-24 | 上海汽车集团股份有限公司 | A kind of vehicle driver's fatigue monitoring early warning system and method |
| CN108670278A (en) * | 2018-05-30 | 2018-10-19 | 东南大学 | A kind of driver fatigue detection and alarm system and method based on smart mobile phone |
| CN108932461A (en) * | 2017-05-27 | 2018-12-04 | 杭州海康威视数字技术股份有限公司 | A kind of fatigue detection method and device |
| CN109017560A (en) * | 2018-07-09 | 2018-12-18 | 中国联合网络通信集团有限公司 | Driving condition analysis method, device and driving prompt system |
| CN109094482A (en) * | 2018-07-25 | 2018-12-28 | 哈尔滨工业大学 | Vehicle operation risk assessment information acquisition system, risk evaluating system and method |
| CN109426757A (en) * | 2017-08-18 | 2019-03-05 | 安徽三联交通应用技术股份有限公司 | Driver's head pose monitoring method, system, medium and equipment based on deep learning |
| CN109466586A (en) * | 2018-11-23 | 2019-03-15 | 周家鸿 | A kind of city rail electric train safe operation means of defence, system and device |
| CN109760690A (en) * | 2019-01-18 | 2019-05-17 | 深圳市趣创科技有限公司 | Method for detecting fatigue driving, system and storage medium |
| CN109803583A (en) * | 2017-08-10 | 2019-05-24 | 北京市商汤科技开发有限公司 | Driver monitoring method, apparatus and electronic equipment |
| CN109833029A (en) * | 2017-11-27 | 2019-06-04 | 深圳创达云睿智能科技有限公司 | A kind of sleep stage method, system and terminal device |
| CN109934189A (en) * | 2019-03-19 | 2019-06-25 | 何世全 | A kind of driving warning method, system and terminal |
| CN110147738A (en) * | 2019-04-29 | 2019-08-20 | 中国人民解放军海军特色医学中心 | A kind of driver fatigue monitoring and pre-alarming method and system |
| CN110525443A (en) * | 2018-05-23 | 2019-12-03 | 罗伯特·博世有限公司 | Method and apparatus for identifying the fatigue of the driver of vehicle in a mobile device |
| CN110579360A (en) * | 2019-10-22 | 2019-12-17 | 东北林业大学 | Automobile control behavior parameter acquisition equipment and method |
| CN110728824A (en) * | 2019-09-25 | 2020-01-24 | 东南大学 | Driver fatigue state detection and reminding method based on multi-source data |
| CN110765807A (en) * | 2018-07-25 | 2020-02-07 | 阿里巴巴集团控股有限公司 | Driving behavior analysis method, driving behavior processing method, driving behavior analysis device, driving behavior processing device and storage medium |
| CN110766755A (en) * | 2019-10-21 | 2020-02-07 | 郑州迈拓信息技术有限公司 | Real-time positioning method for hopper for filling materials of semitrailer |
| CN111145497A (en) * | 2020-01-06 | 2020-05-12 | 广东工业大学 | Portable driver fatigue detection equipment |
| CN111449652A (en) * | 2020-05-06 | 2020-07-28 | 北方工业大学 | Construction safety monitoring method and device based on brain wave analysis |
| CN111832373A (en) * | 2019-05-28 | 2020-10-27 | 北京伟景智能科技有限公司 | Automobile driving posture detection method based on multi-view vision |
| CN112041910A (en) * | 2018-03-30 | 2020-12-04 | 索尼半导体解决方案公司 | Information processing apparatus, mobile device, method, and program |
| CN112829767A (en) * | 2021-02-22 | 2021-05-25 | 清华大学苏州汽车研究院（相城） | Automatic driving control system and method based on monitoring misoperation of driver |
| WO2021159269A1 (en) * | 2020-02-11 | 2021-08-19 | 云图技术有限公司 | Vision-based safe driving early warning method and system, and storage medium |
| CN113327409A (en) * | 2021-05-28 | 2021-08-31 | 上海声通信息科技股份有限公司 | Driving behavior analysis system based on intelligent recognition monitoring |
| CN113762123A (en) * | 2021-08-31 | 2021-12-07 | 同济大学 | Method for detecting driver using mobile phone and computer readable medium |
| CN115973256A (en) * | 2022-11-08 | 2023-04-18 | 哈尔滨理工大学 | Intelligent measurable steering wheel based on body sensor and active early warning method |
| CN116636808A (en) * | 2023-06-28 | 2023-08-25 | 交通运输部公路科学研究所 | Intelligent cockpit driver visual health analysis method and device |
| CN117227740A (en) * | 2023-09-14 | 2023-12-15 | 南京项尚车联网技术有限公司 | Multi-mode sensing system and method for intelligent driving vehicle |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN202142160U (en) * | 2011-07-13 | 2012-02-08 | 上海库源电气科技有限公司 | Fatigue driving early warning system |
| CN102436715A (en) * | 2011-11-25 | 2012-05-02 | 大连海创高科信息技术有限公司 | Detection method for fatigue driving |
| CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
| CN202448833U (en) * | 2011-12-20 | 2012-09-26 | 长安大学 | Fatigue driving early warning device |
| CN103198618A (en) * | 2013-04-13 | 2013-07-10 | 杭州立体世界科技有限公司 | Mobile phone capable of preventing fatigue driving and warning method |
| US20140097957A1 (en) * | 1995-06-07 | 2014-04-10 | American Vehicular Sciences Llc | Driver fatigue monitoring system and method |
| CN103824419A (en) * | 2012-11-16 | 2014-05-28 | 西安众智惠泽光电科技有限公司 | Automobile fatigue driving remote monitoring and real-time prompting system |
| CN104008628A (en) * | 2013-02-25 | 2014-08-27 | 联想(北京)有限公司 | Prompting method and equipment |
-
2014
- 2014-12-25 CN CN201410816357.7A patent/CN104574817A/en active Pending
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140097957A1 (en) * | 1995-06-07 | 2014-04-10 | American Vehicular Sciences Llc | Driver fatigue monitoring system and method |
| CN202142160U (en) * | 2011-07-13 | 2012-02-08 | 上海库源电气科技有限公司 | Fatigue driving early warning system |
| CN102509418A (en) * | 2011-10-11 | 2012-06-20 | 东华大学 | Fatigue driving estimation and early-warning method and device of multi-sensor information fusion |
| CN102436715A (en) * | 2011-11-25 | 2012-05-02 | 大连海创高科信息技术有限公司 | Detection method for fatigue driving |
| CN202448833U (en) * | 2011-12-20 | 2012-09-26 | 长安大学 | Fatigue driving early warning device |
| CN103824419A (en) * | 2012-11-16 | 2014-05-28 | 西安众智惠泽光电科技有限公司 | Automobile fatigue driving remote monitoring and real-time prompting system |
| CN104008628A (en) * | 2013-02-25 | 2014-08-27 | 联想(北京)有限公司 | Prompting method and equipment |
| CN103198618A (en) * | 2013-04-13 | 2013-07-10 | 杭州立体世界科技有限公司 | Mobile phone capable of preventing fatigue driving and warning method |
Non-Patent Citations (1)
| Title |
|---|
| 张伟: "基于机器视觉的驾驶人疲劳状态识别关键问题研究", 《中国博士学位论文全文数据库工程科技Ⅱ辑》 * |
Cited By (66)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105034804A (en) * | 2015-05-11 | 2015-11-11 | 南京理工大学 | Vehicle active safety control method and vehicle active safety control system |
| CN105035074A (en) * | 2015-06-23 | 2015-11-11 | 南京理工大学 | Vehicle active safety control method based on portable intelligent equipment |
| CN106326912A (en) * | 2015-07-01 | 2017-01-11 | 埃西勒国际通用光学公司 | A user fatigue level analysis component |
| CN105373767A (en) * | 2015-07-23 | 2016-03-02 | 中山大学深圳研究院 | Eye fatigue detection method for smart phones |
| US10768591B2 (en) | 2015-09-03 | 2020-09-08 | Mitsubishi Electric Corporation | Behavior identification device, air conditioner, and robot control device |
| CN107708553A (en) * | 2015-09-03 | 2018-02-16 | 三菱电机株式会社 | Activity recognition device, air conditioner and robot controller |
| CN105303830A (en) * | 2015-09-15 | 2016-02-03 | 成都通甲优博科技有限责任公司 | Driving behavior analysis system and analysis method |
| CN105261152A (en) * | 2015-09-30 | 2016-01-20 | 中国民用航空总局第二研究所 | Air traffic controller fatigue detection method based on clustering analysis, device and system |
| CN105261152B (en) * | 2015-09-30 | 2018-01-30 | 中国民用航空总局第二研究所 | Air traffic controller's fatigue detection method based on cluster analysis, device and system |
| CN108349494A (en) * | 2015-10-31 | 2018-07-31 | 戴姆勒股份公司 | Run the distance of vehicle and the method for speed adjustment function and the driver assistance system for executing this method |
| CN108349494B (en) * | 2015-10-31 | 2021-07-13 | 戴姆勒股份公司 | Method for operating a distance and speed adjustment function of a vehicle and driver assistance system for carrying out the method |
| CN105261153A (en) * | 2015-11-03 | 2016-01-20 | 北京奇虎科技有限公司 | Vehicle running monitoring method and device |
| CN105512613A (en) * | 2015-11-26 | 2016-04-20 | 中山大学 | Smartphone-based eye fatigue detection method |
| CN105718875A (en) * | 2016-01-15 | 2016-06-29 | 广东欧珀移动通信有限公司 | Visual fatigue detection method and user terminal |
| CN106226556A (en) * | 2016-07-06 | 2016-12-14 | 深圳市元征科技股份有限公司 | Acceleration transducer calibration steps and device |
| CN106226556B (en) * | 2016-07-06 | 2020-07-10 | 深圳市元征科技股份有限公司 | Acceleration sensor calibration method and device |
| WO2018014598A1 (en) * | 2016-07-18 | 2018-01-25 | 中兴通讯股份有限公司 | Method and terminal for alerting fatigued driver |
| CN106627459A (en) * | 2016-08-30 | 2017-05-10 | 湖北文理学院 | Automobile safety warning and accident warning based on intelligent device and location method |
| CN106627459B (en) * | 2016-08-30 | 2018-10-30 | 湖北文理学院 | A kind of automotive safety warning and accident alarming and localization method based on smart machine |
| WO2018058266A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Mobile terminal-based notification method and system |
| WO2018058275A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Smart driving method and system employing mobile terminal |
| WO2018058273A1 (en) * | 2016-09-27 | 2018-04-05 | 深圳智乐信息科技有限公司 | Anti-theft method and system |
| CN106778532A (en) * | 2016-11-28 | 2017-05-31 | 合肥工业大学 | Based on the driving posture feature sorting technique for removing differentiation size parameter |
| CN106778532B (en) * | 2016-11-28 | 2019-05-31 | 合肥工业大学 | Based on the driving posture feature classification method for removing differentiation size parameter |
| CN106710145A (en) * | 2016-12-29 | 2017-05-24 | 清华大学苏州汽车研究院（吴江） | Guided driver tiredness prevention method |
| CN106710145B (en) * | 2016-12-29 | 2019-10-25 | 清华大学苏州汽车研究院（吴江） | A kind of guiding driver drowsiness prevention method |
| CN108932461A (en) * | 2017-05-27 | 2018-12-04 | 杭州海康威视数字技术股份有限公司 | A kind of fatigue detection method and device |
| CN107392159A (en) * | 2017-07-27 | 2017-11-24 | 竹间智能科技（上海）有限公司 | A kind of facial focus detecting system and method |
| CN109803583A (en) * | 2017-08-10 | 2019-05-24 | 北京市商汤科技开发有限公司 | Driver monitoring method, apparatus and electronic equipment |
| CN109426757A (en) * | 2017-08-18 | 2019-03-05 | 安徽三联交通应用技术股份有限公司 | Driver's head pose monitoring method, system, medium and equipment based on deep learning |
| CN107618512A (en) * | 2017-08-23 | 2018-01-23 | 清华大学 | Driving behavior safe evaluation method based on people's car environment multi-data source |
| CN109833029A (en) * | 2017-11-27 | 2019-06-04 | 深圳创达云睿智能科技有限公司 | A kind of sleep stage method, system and terminal device |
| CN108446600A (en) * | 2018-02-27 | 2018-08-24 | 上海汽车集团股份有限公司 | A kind of vehicle driver's fatigue monitoring early warning system and method |
| CN112041910A (en) * | 2018-03-30 | 2020-12-04 | 索尼半导体解决方案公司 | Information processing apparatus, mobile device, method, and program |
| CN112041910B (en) * | 2018-03-30 | 2023-08-18 | 索尼半导体解决方案公司 | Information processing apparatus, mobile device, method, and program |
| CN110525443A (en) * | 2018-05-23 | 2019-12-03 | 罗伯特·博世有限公司 | Method and apparatus for identifying the fatigue of the driver of vehicle in a mobile device |
| CN108670278A (en) * | 2018-05-30 | 2018-10-19 | 东南大学 | A kind of driver fatigue detection and alarm system and method based on smart mobile phone |
| CN109017560A (en) * | 2018-07-09 | 2018-12-18 | 中国联合网络通信集团有限公司 | Driving condition analysis method, device and driving prompt system |
| CN109094482A (en) * | 2018-07-25 | 2018-12-28 | 哈尔滨工业大学 | Vehicle operation risk assessment information acquisition system, risk evaluating system and method |
| CN110765807A (en) * | 2018-07-25 | 2020-02-07 | 阿里巴巴集团控股有限公司 | Driving behavior analysis method, driving behavior processing method, driving behavior analysis device, driving behavior processing device and storage medium |
| CN109094482B (en) * | 2018-07-25 | 2021-08-24 | 哈尔滨工业大学 | Vehicle operation risk assessment information acquisition system, risk assessment system and method |
| CN110765807B (en) * | 2018-07-25 | 2024-04-05 | 斑马智行网络(香港)有限公司 | Driving behavior analysis and processing method, device, equipment and storage medium |
| CN109466586A (en) * | 2018-11-23 | 2019-03-15 | 周家鸿 | A kind of city rail electric train safe operation means of defence, system and device |
| CN109760690A (en) * | 2019-01-18 | 2019-05-17 | 深圳市趣创科技有限公司 | Method for detecting fatigue driving, system and storage medium |
| CN109934189A (en) * | 2019-03-19 | 2019-06-25 | 何世全 | A kind of driving warning method, system and terminal |
| CN109934189B (en) * | 2019-03-19 | 2021-08-13 | 山东同云网络科技有限公司 | Driving early warning method, system and terminal |
| CN110147738A (en) * | 2019-04-29 | 2019-08-20 | 中国人民解放军海军特色医学中心 | A kind of driver fatigue monitoring and pre-alarming method and system |
| CN111832373A (en) * | 2019-05-28 | 2020-10-27 | 北京伟景智能科技有限公司 | Automobile driving posture detection method based on multi-view vision |
| CN110728824A (en) * | 2019-09-25 | 2020-01-24 | 东南大学 | Driver fatigue state detection and reminding method based on multi-source data |
| CN110728824B (en) * | 2019-09-25 | 2021-11-30 | 东南大学 | Driver fatigue state detection and reminding method based on multi-source data |
| CN110766755A (en) * | 2019-10-21 | 2020-02-07 | 郑州迈拓信息技术有限公司 | Real-time positioning method for hopper for filling materials of semitrailer |
| CN110579360A (en) * | 2019-10-22 | 2019-12-17 | 东北林业大学 | Automobile control behavior parameter acquisition equipment and method |
| CN111145497A (en) * | 2020-01-06 | 2020-05-12 | 广东工业大学 | Portable driver fatigue detection equipment |
| WO2021159269A1 (en) * | 2020-02-11 | 2021-08-19 | 云图技术有限公司 | Vision-based safe driving early warning method and system, and storage medium |
| CN111449652B (en) * | 2020-05-06 | 2022-11-29 | 北方工业大学 | Construction safety monitoring method and device based on brain wave analysis |
| CN111449652A (en) * | 2020-05-06 | 2020-07-28 | 北方工业大学 | Construction safety monitoring method and device based on brain wave analysis |
| CN112829767A (en) * | 2021-02-22 | 2021-05-25 | 清华大学苏州汽车研究院（相城） | Automatic driving control system and method based on monitoring misoperation of driver |
| CN112829767B (en) * | 2021-02-22 | 2024-05-17 | 清华大学苏州汽车研究院（相城） | Automatic driving control system and method based on monitoring of misoperation of driver |
| CN113327409A (en) * | 2021-05-28 | 2021-08-31 | 上海声通信息科技股份有限公司 | Driving behavior analysis system based on intelligent recognition monitoring |
| CN113762123A (en) * | 2021-08-31 | 2021-12-07 | 同济大学 | Method for detecting driver using mobile phone and computer readable medium |
| CN113762123B (en) * | 2021-08-31 | 2022-11-18 | 同济大学 | Method for detecting driver using mobile phone and computer readable medium |
| CN115973256A (en) * | 2022-11-08 | 2023-04-18 | 哈尔滨理工大学 | Intelligent measurable steering wheel based on body sensor and active early warning method |
| CN116636808B (en) * | 2023-06-28 | 2023-10-31 | 交通运输部公路科学研究所 | Intelligent cockpit driver visual health analysis method and device |
| CN116636808A (en) * | 2023-06-28 | 2023-08-25 | 交通运输部公路科学研究所 | Intelligent cockpit driver visual health analysis method and device |
| CN117227740A (en) * | 2023-09-14 | 2023-12-15 | 南京项尚车联网技术有限公司 | Multi-mode sensing system and method for intelligent driving vehicle |
| CN117227740B (en) * | 2023-09-14 | 2024-03-19 | 南京项尚车联网技术有限公司 | Multi-mode sensing system and method for intelligent driving vehicle |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104574817A (en) | Machine vision-based fatigue driving pre-warning system suitable for smart phone | |
| Ramzan et al. | A survey on state-of-the-art drowsiness detection techniques | |
| JP6929366B2 (en) | Driver monitoring and response system | |
| Bergasa et al. | Real-time system for monitoring driver vigilance | |
| CN105286802B (en) | Driver Fatigue Detection based on video information | |
| CN202257856U (en) | Driver fatigue-driving monitoring device | |
| CN105769120A (en) | Fatigue driving detection method and device | |
| CN102982316A (en) | Driver abnormal driving behavior recognition device and method thereof | |
| CN112434611B (en) | Early fatigue detection method and system based on eye movement subtle features | |
| Flores et al. | Driver drowsiness detection system under infrared illumination for an intelligent vehicle | |
| CN109389806A (en) | Fatigue driving detection method for early warning, system and medium based on multi-information fusion | |
| CN104224204A (en) | Driver fatigue detection system on basis of infrared detection technology | |
| Wang et al. | Automobile driver posture monitoring systems: A review | |
| CN109740477A (en) | Study in Driver Fatigue State Surveillance System and its fatigue detection method | |
| CN104881956A (en) | Fatigue driving early warning system | |
| Lashkov et al. | Driver dangerous state detection based on OpenCV & dlib libraries using mobile video processing | |
| Ursulescu et al. | Driver drowsiness detection based on eye analysis | |
| Nakamura et al. | Driver drowsiness estimation from facial expression features computer vision feature investigation using a CG model | |
| Bergasa et al. | Visual monitoring of driver inattention | |
| Zhang et al. | Driver fatigue state detection based on facial key points | |
| Rahman et al. | Computer vision-based approach to detect fatigue driving and face mask for edge computing device | |
| Solomon et al. | Driver Attention and Behavior Detection with Kinect | |
| CN203885510U (en) | Driver fatigue detection system based on infrared detection technology | |
| Yoon et al. | Driver’s eye-based gaze tracking system by one-point calibration | |
| Diddi et al. | Head pose and eye state monitoring (HEM) for driver drowsiness detection: Overview |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20150429 |