CN107016343A - A kind of traffic lights method for quickly identifying based on Bel's format-pattern - Google Patents
A kind of traffic lights method for quickly identifying based on Bel's format-pattern Download PDFInfo
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- CN107016343A CN107016343A CN201710128988.3A CN201710128988A CN107016343A CN 107016343 A CN107016343 A CN 107016343A CN 201710128988 A CN201710128988 A CN 201710128988A CN 107016343 A CN107016343 A CN 107016343A
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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
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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Abstract
The present invention relates to a kind of traffic lights method for quickly identifying based on Bel's format-pattern, the characteristics of this method utilizes Bel's format-pattern, without difference operation, R channel images and G channel images are directly extracted from image, after handling image, comprehensive area, position, dutycycle, circularity, red green passage gray scale than etc. parameter judge whether the current states of traffic lights and traffic lights.This method is handled image using different Color Channels, reduces the interference between different color channels, with stronger robustness；Without complicated algorithm for pattern recognition and mathematical operation, with stronger real-time.
Description
【Technical field】
The invention belongs to the pattern-recognition of computer vision and artificial intelligence field, it is related to a kind of for image recognition and mesh
The method for marking information extraction, more particularly to a kind of traffic lights method for quickly identifying based on Bel's format-pattern.
【Background technology】
Using the method for Digital Image Processing realize Target Segmentation, pattern-recognition computer vision field be it is a kind of very
Universal application.In recent years, the research and development of pilotless automobile promotes people increasingly to pay close attention in urban traffic environment
Traffic lights recognize problem.
Existing traffic light identification method is usually the coloured image gathered from camera, at suitable image
Adjustment method, extracts specific features in image, and then realize the identification of traffic lights.There are two kinds of conventional recognition methods at present.First,
It is the recognition methods based on color space, this method extracts pixel characteristic from the color spaces such as RGB, HIS, Lab, and then judges
The state of traffic lights.Second, being the method based on machine learning, such as Adaboost and neutral net.This method is first to big
The training sample of amount extracts the notable feature of traffic lights, carries out pattern classification, generates specific criterion, then camera is adopted
Collect obtained new image accordingly to be judged.The degree of accuracy of this method is of a relatively high, but needs substantial amounts of training sample
With the off-line learning of longer time, and calculate complicated.
Therefore, a kind of higher traffic lights recognizer of easy and effective, robustness is designed particularly significant.
【The content of the invention】
The invention provides a kind of traffic lights method for quickly identifying based on Bel's format-pattern, this method is in traditional figure
On the basis of processing, recognizer is designed based on Bel's format-pattern, the degree of accuracy and the speed of traffic lights identification is improved.
The present invention uses following technical scheme：
A kind of traffic lights method for quickly identifying based on Bel's format-pattern, comprises the following steps：
(1) fixed, calibration for cameras；
(2) image is gathered, candidate region is determined：
Red channel component and green channel component in collection image is extracted according to Bel's format-pattern, extracted in image
Connected domain, using each connected domain as candidate region, other dark spaces are considered as background area；
(3) traffic lights are recognized：
Most non-traffic lights region in image is excluded first, and dutycycle and circle are used for remaining candidate region
Degree determines the region of approximate prototype, and traffic lights are determined finally by red green passage gray value ratio.
Further, in step (2), extract after red channel component and green channel component, extract speck in image
Part, using connected domain algorithm, extracts connected domain part in image, is used as candidate region.
Further, in step (3), according to traffic lights elemental area in the picture or the lamp center pixel coordinate of traffic lights
Position excludes most of non-traffic lights region.
Further, the elemental area of described traffic lights in the picture is calculated according to following：
The diameter d of traffic lights in the picture：
Wherein, d is the diameter of traffic lights in the picture, in units of pixel, and f is the equivalent focal length of camera, f0It is camera
The real focal length of camera lens, dxIt is the size of camera；ZcIt is the distance between camera and traffic lights；
According to the parameter of camera, camera just can be estimated apart from traffic lights ZcWhen, the external square of traffic lights in the picture
Shape region and size.
Further, the lamp center pixel coordinate position of described traffic lights is calculated according to following：
The lamp center pixel coordinate of traffic lights should meet following constraint：
Wherein, x, y are lamp center pixel coordinate, and width, height is respectively the wide and height of image.
Further, in step (3),
The dutycycle R of candidate region is：
Wherein, S is candidate region, SrFor the area of the boundary rectangle of candidate region；
The circularity of candidate region is C：
Wherein, L is the girth of candidate region.
Further, in step (3), the gray scale ratio of candidate region traffic lights is：
Wherein R is the R passage gray value averages of candidate region, and G is the G passage gray value averages of candidate region；
When K is much larger than 1, it is believed that candidate region is red light；When K is much smaller than 1, it is believed that candidate region is green light；K close to
When 1, it is believed that candidate region is amber light.
The optical axis of camera is consistent with vehicle body direction.
After the parameter of calibration for cameras, the aperture and focal length of camera lens are adjusted, it is ensured that the clear presentation of image.
After the parameter of calibration for cameras, adjust the imaging parameters of camera, prominent traffic lights, close camera automatic exposure and from
Dynamic gain function, reduces time for exposure and the gain of camera, image overall brightness is significantly declined.
Compared with prior art, the present invention at least has the advantages that：The present invention is a kind of based on Bel's format chart
As subchannel, simple and effective traffic light identification method, have the characteristics that：First, it is divided to two passages to be individually identified, greatly
The interference of the color of other in coloured image is reduced, intensity larger red component and green component is only remained, illumination is become
Change is insensitive, is easy to fast and accurately position candidate region, and without using complicated algorithm for pattern recognition.
【Brief description of the drawings】
Fig. 1 is Bel's form original image schematic diagram；
Fig. 2 is traffic lights identification process figure of the invention.
【Embodiment】
It is an object of the invention to design a kind of traffic lights recognizer based on Bel's format-pattern, rapidly and accurately know
Traffic lights mark in not real traffic environment.
Current color image sensor (CMOS or CCD) can only gather the one-component of RGB color in each pixel,
Realized by using color filter array, the pixel for spraying red filter material only passes through feux rouges, sprays green filter material
Pixel only pass through green glow.
According to These characteristics, the present invention proposes following technical scheme：
First, the hardware used in the present invention is briefly described.According to application demand, suitable camera, phase are selected
The resolution ratio of machine should ensure that subchannel extracts the definition of image, and optical axis and the vehicle body direction of camera are consistent, in terms of reducing
Calculate complexity.Camera gathers image, passes to dsp chip and carries out analysis judgement, exports end product.
The detailed implementation steps of the present invention are described below：
The first step, fixed camera.
According to the actual requirements, suitable camera is selected, it is desirable to which camera has higher resolution ratio and data transmission bauds, with
Ensure the precision and real-time of detection.Then fixed camera, makes its optical axis parallel to vehicle body direction.
Second step, calibration for cameras inside and outside parameter adjusts camera imaging parameter.
Using gridiron pattern scaling board, the inside and outside parameter of calibration for cameras, and the aperture and focal length of camera lens are adjusted, protected
Demonstrate,prove the clear presentation of image.Then the imaging parameters of camera are adjusted, to protrude traffic lights, and suppress the purpose of other interference.
Automatic exposure and the automatic gain function of camera are closed, time for exposure and the gain of camera are reduced as far as possible, makes image overall brightness
Significantly decline, because traffic lights are artificial light sources, therefore the red channel intensity of red light is very big, the green channel intensity of green light
It is very big, compared to the red background and green background in scene, when the brightness entire lowering of image, the weaker red back of the body of intensity
Scape is disturbed and green background interference just fades away, and the brightness of red light and green light is still relatively strong, this phenomenon table in the picture
Now there is obvious speck for the correspondence position of traffic lights, other positions brightness is relatively low, close to black.Last set image is adopted
Collect frame per second, frame per second is too high, increases the degree of redundancy of program；Frame per second is too low, reduces the sensitivity of identification, in practice, frame per second
It is more suitable for 10~20fps.
3rd step, gathers image, extracts red channel component and green channel component, determines candidate region, preliminary to judge.
1st, recognize traffic lights when, according to the traffic lights image gathered, extracted respectively for original image G channel components and
R channel components, obtain the image under different passages, extract the connected domain in image, are entered using each connected domain as candidate region
Row traffic lights recognize, the then position of the scope of elemental area, traffic lights in the picture according to shared by the image under two passages
And the dutycycle and circularity of candidate region carry out the judgement of traffic lights.Extract red channel component and green channel component
The original image for Bel's form that COMS or CCD is obtained, as shown in Figure 1.For tetra- pixels of G1, R2, B6, G7
Point, R2 represents R passage pixels, and G1, G7 represent G passage pixels, and acquisition resolution ratio is original image a quarter R passage figures
Picture and G channel images.
2nd, candidate region is determined
Mean filter progress denoising is used firstly, for the picture of extraction.Then binaryzation is carried out to image, afterwards
The part of speck in image is extracted, using connected domain algorithm, connected domain part in image is extracted, using it as candidate region, and its
He is considered as background area in dark space.When subchannel obtains candidate region, red green passage is directly isolated by Bel's format-pattern,
Rather than interpolated obtain red green passage.For tetra- pixels of G1, R2, B6, G7, its R passage is characterized with R2 values, with G1 or G7 values
Its G passage is characterized, it is original image a quarter R channel images and G channel images to obtain resolution ratio.
3rd, traffic lights tentatively judge
Each candidate region is analyzed respectively, comprehensive a variety of discrimination standards provide present image with the presence or absence of red
The final judgement of green light.According to some prioris of traffic lights in City scenarios, following several decision criterias can be obtained：
1) according to area recognition traffic lights
In urban transportation, the normal diameter D of traffic lights is 20cm or so, and the standard identification distance about 20m of traffic lights is extremely
80m, then according to below equation, try to achieve the elemental area of traffic lights in the picture：
Wherein, d is the diameter of traffic lights in the picture, in units of pixel, and f is the equivalent focal length of camera, f0It is camera
The real focal length of camera lens, dxIt is camera CMOS or CCD size；According to the parameter of camera, camera just can be estimated apart from red
Green light ZcWhen, traffic lights circumscribed rectangular region in the picture and size.According to this area, excessive or mistake cell can be excluded
Domain.
2) traffic lights are recognized according to the position of traffic lights
Traffic lights are typically in road both sides in the position in city, and have specific height.It is red when driving conditions are shot
Green light is in the top of image, i.e. the lamp center pixel coordinate of traffic lights should meet following constraint：
Wherein x, y are lamp center pixel coordinate, and width, height is the wide and height of image.
To the candidate region of extraction, usable floor area and position are judged respectively, filter out the time for being unsatisfactory for area and position
Favored area, retains the region met, the PRELIMINARY RESULTS judged as traffic lights.
4th, synthetic determination output result
After simple preliminary judgement, most of non-traffic lights region is excluded, and remaining region is less, to these times
Favored area carries out final fine calculating and analysis, finally provides the recognition result of traffic lights.
1) dutycycle and circularity
Circular traffic lights, are in the picture ellipse due to there is perspective transform after camera imaging, but by
In on real road, camera is usually that, to face the angle shot of traffic lights, perspective phenomenon is not obvious, therefore traffic lights are in figure
Corresponding region is approximately circular as in.
Define candidate region dutycycle be：
Wherein S is candidate region, SrFor the area of the boundary rectangle of candidate region；
Define candidate region circularity be：Wherein, L is the girth of candidate region.
In theory, the dutycycle R and circularity C of border circular areas are 1, but be due to by noise, distortion, image algorithm shadow
Ring, R and C can be fluctuated near 1.By the two parameters, the region of approximate prototype just can determine that.
2) red green passage gray value ratio
For the traffic lights of candidate region, gray value ratio is：Wherein R is equal for the R passage gray values of candidate region
Value, G is the G passage gray value averages of candidate region.When K is much larger than 1, it is believed that candidate region is red light；When K is much smaller than 1, recognize
It is green light for candidate region；When K is close to 1, it is believed that candidate region is amber light.
Because red light and green light color distortion are larger, therefore the corresponding region that red light will not be on green channel images
There is high intensity speck, similarly green light also high intensity speck will not occur in the corresponding region of red channel image, can be according to bright
The passage that spot occurs judges traffic lights, while the speck for appearing in two passages is probably the larger street lamp of brightness, searchlight etc.,
Rather than traffic lights.
In summary discrimination standard, just can accurately judge whether there is traffic lights in image, criterion is provided for decision-making.
Because the present invention is to obtain candidate region by Bel's color format image subchannel, so that detection part use only simply
Image processing algorithm, such as filtering, connected domain are not directed to complex algorithm for pattern recognition, and judging section does not have yet
The mathematical operation of complexity is related to, therefore with stronger real-time.
A kind of implementation of the present invention is the foregoing is only, is not all of or unique embodiment, art technology
Personnel, by any equivalent conversion read description of the invention and taken technical solution of the present invention, are the power of the present invention
Profit requires to be covered.
Claims (10)
1. a kind of traffic lights method for quickly identifying based on Bel's format-pattern, it is characterised in that：Comprise the following steps：
(1) fixed, calibration for cameras；
(2) image is gathered, candidate region is determined：
Red channel component and green channel component in collection image is extracted according to Bel's format-pattern, the company in image is extracted
Logical domain, using each connected domain as candidate region, other dark spaces are considered as background area；
(3) traffic lights are recognized：
Most non-traffic lights region in image is excluded first, it is true using dutycycle and circularity for remaining candidate region
The region of approximate prototype is made, traffic lights are determined finally by red green passage gray value ratio.
2. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 1, it is characterised in that：
In step (2), extract after red channel component and green channel component, extract the part of speck in image, use connected domain
Algorithm, extracts connected domain part in image, is used as candidate region.
3. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 1, it is characterised in that：
In step (3), big portion is excluded according to the lamp center pixel coordinate position of traffic lights elemental area in the picture or traffic lights
Divide non-traffic lights region.
4. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 3, it is characterised in that：
The elemental area of described traffic lights in the picture is calculated according to following：
The diameter d of traffic lights in the picture：
Wherein, d is the diameter of traffic lights in the picture, in units of pixel, and f is the equivalent focal length of camera, f0It is camera lens
Real focal length, dxIt is the size of camera；ZcIt is the distance between camera and traffic lights；
According to the parameter of camera, camera just can be estimated apart from traffic lights ZcWhen, the circumscribed rectangular region of traffic lights in the picture
And size.
5. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 3, it is characterised in that：
The lamp center pixel coordinate position of described traffic lights is calculated according to following：
The lamp center pixel coordinate of traffic lights should meet following constraint：
Wherein, x, y are lamp center pixel coordinate, and width, height is respectively the wide and height of image.
6. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 1, it is characterised in that：
In step (3),
The dutycycle R of candidate region is：
Wherein, S is candidate region, SrFor the area of the boundary rectangle of candidate region；
The circularity of candidate region is C：
Wherein, L is the girth of candidate region.
7. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to claim 6, it is characterised in that：
In step (3), the gray scale ratio of candidate region traffic lights is：
Wherein R is the R passage gray value averages of candidate region, and G is the G passage gray value averages of candidate region；
When K is much larger than 1, it is believed that candidate region is red light；When K is much smaller than 1, it is believed that candidate region is green light；When K is close to 1,
It is amber light to think candidate region.
8. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to any one of claim 1 to 7,
It is characterized in that：The optical axis of camera is consistent with vehicle body direction.
9. a kind of traffic lights method for quickly identifying based on Bel's format-pattern according to any one of claim 1 to 7,
It is characterized in that：After the parameter of calibration for cameras, the aperture and focal length of camera lens are adjusted, it is ensured that the clear presentation of image.
10. a kind of quick side of identification of traffic lights based on Bel's format-pattern according to any one of claim 1 to 7
Method, it is characterised in that：After the parameter of calibration for cameras, the imaging parameters of camera are adjusted, prominent traffic lights close the automatic exposure of camera
Light and automatic gain function, reduce time for exposure and the gain of camera, image overall brightness is significantly declined.
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| CN110555342A (en) * | 2018-05-31 | 2019-12-10 | 杭州海康威视数字技术股份有限公司 | image identification method and device and image equipment |
| CN111355936A (en) * | 2018-12-20 | 2020-06-30 | 杭州凝眸智能科技有限公司 | Method and system for acquiring and processing image data for artificial intelligence |
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