CN109977820A - A kind of fatigue driving determination method - Google Patents
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- G06V20/597—Recognising the driver's state or behaviour, e.g. attention or drowsiness
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- G06V40/168—Feature extraction; Face representation
- G06V40/171—Local features and components; Facial parts ; Occluding parts, e.g. glasses; Geometrical relationships
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Abstract
The present invention relates to a kind of fatigue driving determination methods, belong to field of image processing.This method is divided into three parts: driver's mouth fatigue detecting, driver eye's fatigue detecting and driver's entirety fatigue driving determine.This method aims to solve the problem that existing verification and measurement ratio is not high, the poor fatigue detecting that can only be solved the problems, such as under single-frame images of practicability.Better detection effect can be reached in the test of data set.In addition, this method can also provide technical support for the in-vehicle device embedded development of vehicle-mounted camera etc.
Description
Technical field
The invention belongs to field of image processings, are related to the technologies such as embedded development, the intelligent automobile of in-vehicle device, main needle
The fatigue state of interior driver is determined.
Background technique
With the continuous improvement of car ownership, traffic safety accident problem have become current social face it is tight
Weight problem.Currently, China's road traffic accident year death toll Yuan Chao other countries, are in second place of the world, containment road is handed over
Interpreter's event is high-incidence, reduces traffic accident injury shoulders heavy responsibilities.In huge traffic accident death data behind, driver fatigue is driven
Sailing is one of the main reasons, therefore is detected to driver fatigue state and make early warning and have great meaning.
Existing fatigue driving system includes three parts: Face datection, human eye detection and fatigue determine.Wherein, face
Detection uses Haar and AdaBoost algorithm to detect face, and human eye detection uses gray-level projection and block analysis of complexity
In conjunction with positioning human eye.Then PERCLOS value is calculated to the human eye detected to judge fatigue state.But this method there is
Verification and measurement ratio is not high, the poor fatigue detecting that can only be solved the problems, such as under single-frame images of practicability.Therefore, high reliablity and energy are studied
There are great researching value and realistic meaning for the algorithm of multiple image detection.
Summary of the invention
In view of this, effectively detecting driving procedure the purpose of the present invention is to provide a kind of fatigue driving determination method
The fatigue state of middle driver, and make early warning.
In order to achieve the above objectives, the invention provides the following technical scheme:
A kind of fatigue driving determination method, including following part:
A: driver's mouth fatigue state is detected；
B: driver eye's fatigue state is detected；
C: driver's entirety fatigue state is determined.
Further, the part a the following steps are included:
A1: 4 layers of convolutional neural networks CNN model are used, database is modeled；
A2: for given driver's mouth picture, mouth fatigue state testing result is exported.
Further, the part b includes:
For given eye feature point, eyes closed degree EAR is calculated, formula is as follows:
In formula, P1,P2,P3,P4,P5,P6It is respectively to rotate totally 6 points counterclockwise from left eye angle.
Further, the part c specifically includes the following steps:
C1: for given driver's image, take 30 frame images as a window sequence；
C2: ask eye and mouth fatigue accounting, formula in window as follows respectively:
In formula, C indicates the totalframes in the window, c1Indicate that eye is judged as the frame number of fatigue, c2Indicate that mouth is judged to
It is set to the frame number of fatigue, r1Indicate the ratio of the total window frame number of eye strain frame number Zhan, r2Indicate the total window of mouth fatigue frame number Zhan
The ratio of frame number；
C3: according to the accounting acquired and given weight, the whole tired decision content of driver is calculated, formula is as follows:
D=w1×r1+w2×r2
In formula, D indicates that the fatigue acquired determines as a result, w1And w2Respectively indicate the weight of eye feature and mouth feature, r1
And r2Respectively indicate the ratio that eye strain and the frame number of mouth fatigue in current window account for window totalframes.
The beneficial effects of the present invention are: the present invention can be realized more stable than the prior art and reliable detection knot
Fruit, and it is more suitable for the exploitation of interior embedded device at this stage.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is convolutional neural networks (CNN) illustraton of model of the invention；
Fig. 2 is eye feature point ordering chart of the invention；
Fig. 3 is the whole tired determination method flow chart of driver of the invention.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.
Wherein, the drawings are for illustrative purposes only and are merely schematic diagrams, rather than pictorial diagram, should not be understood as to this
The limitation of invention；Embodiment in order to better illustrate the present invention, the certain components of attached drawing have omission, zoom in or out, not
Represent the size of actual product；It will be understood by those skilled in the art that certain known features and its explanation may be omitted and be in attached drawing
It is understood that.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；It is retouched in of the invention
In stating, it is to be understood that if there is the orientation or positional relationship of the instructions such as term " on ", "lower", "left", "right", "front", "rear"
To be based on the orientation or positional relationship shown in the drawings, be merely for convenience of description of the present invention and simplification of the description, rather than indicate or
It implies that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore is described in attached drawing
The term of positional relationship only for illustration, is not considered as limiting the invention, for the ordinary skill of this field
For personnel, the concrete meaning of above-mentioned term can be understood as the case may be.
The present invention provides a kind of fatigue driving determination method, including following part:
A: driver's mouth fatigue state is detected；
B: driver eye's fatigue state is detected；
C: driver's entirety fatigue state is determined.
As shown in Figure 1, the present invention provides the mouth fatigue detecting CNN illustraton of model in a kind of fatigue driving determination method.Tool
Body includes following part:
2 convolutional layers, 2 full articulamentums and 2 pond layers；
Activation primitive uses ReLU function, and pond layer is using maximum pond.
Mouth fatigue state is detected the following steps are included:
A1: 4 layers of convolutional neural networks (CNN) model are used, database is modeled；
A2: for given driver's mouth picture, mouth fatigue state testing result is exported.
As shown in Fig. 2, the present invention provides the eye feature point ordering chart in a kind of fatigue driving determination method.Wherein eyes
It closes and to spend calculation formula as follows:
In formula, P1,P2,P3,P4,P5,P6It is respectively to rotate totally 6 points counterclockwise from left eye angle.
As described in Figure 3, the present invention provides a kind of whole tired determination method stream of the driver in fatigue driving determination method
Cheng Tu.Wherein data processing section the following steps are included:
C1: for given driver's image, take 30 frame images as a window sequence；
C2: ask eye and mouth fatigue accounting, formula in window as follows respectively:
In formula, C indicates the totalframes in the window, c1Indicate that eye is judged as the frame number of fatigue, c2Indicate that mouth is judged to
It is set to the frame number of fatigue, r1Indicate the ratio of the total window frame number of eye strain frame number Zhan, r2Indicate the total window of mouth fatigue frame number Zhan
The ratio of frame number.
C3: according to the accounting acquired and given weight, the whole tired decision content of driver is calculated, formula is as follows:
D=w1×r1+w2×r2
In formula, D indicates that the fatigue acquired determines as a result, w1And w2Respectively indicate the weight of eye feature and mouth feature, r1
And r2Respectively indicate the ratio that eye strain and the frame number of mouth fatigue in current window account for window totalframes.
If D is greater than given threshold θ, current driver's are in a state of fatigue, provide fatigue driving early warning.If small
In threshold θ, then window moves back, and returns to step c1.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (4)
1. a kind of fatigue driving determination method, it is characterised in that: including following part:
A: driver's mouth fatigue state is detected；
B: driver eye's fatigue state is detected；
C: driver's entirety fatigue state is determined.
2. a kind of according to claim 1, fatigue driving determination method, it is characterised in that: the part a includes following step
It is rapid:
A1: 4 layers of convolutional neural networks CNN model are used, database is modeled；
A2: for given driver's mouth picture, mouth fatigue state testing result is exported.
3. a kind of according to claim 1, fatigue driving determination method, it is characterised in that: the part b includes:
For given eye feature point, eyes closed degree EAR is calculated, formula is as follows:
In formula, P1,P2,P3,P4,P5,P6It is respectively to rotate totally 6 points counterclockwise from left eye angle.
4. a kind of according to claim 1, fatigue driving determination method, it is characterised in that: the part c specifically include with
Lower step:
C1: for given driver's image, take 30 frame images as a window sequence；
C2: ask eye and mouth fatigue accounting, formula in window as follows respectively:
In formula, C indicates the totalframes in the window, c1Indicate that eye is judged as the frame number of fatigue, c2Indicate that mouth is judged as
The frame number of fatigue, r1Indicate the ratio of the total window frame number of eye strain frame number Zhan, r2Indicate the total window frame number of mouth fatigue frame number Zhan
Ratio；
C3: according to the accounting acquired and given weight, the whole tired decision content of driver is calculated, formula is as follows:
D=w1×r1+w2×r2
In formula, D indicates that the fatigue acquired determines as a result, w1And w2Respectively indicate the weight of eye feature and mouth feature, r1And r2
Respectively indicate the ratio that eye strain and the frame number of mouth fatigue in current window account for window totalframes.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111179551A (en) * | 2019-12-17 | 2020-05-19 | 西安工程大学 | Real-time monitoring method for dangerous chemical transport driver |
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| CN111179551A (en) * | 2019-12-17 | 2020-05-19 | 西安工程大学 | Real-time monitoring method for dangerous chemical transport driver |
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Application publication date: 20190705 |