CN105512662A - Detection method and apparatus for unlicensed vehicle - Google Patents
Detection method and apparatus for unlicensed vehicle Download PDFInfo
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- CN105512662A CN105512662A CN201510850001.XA CN201510850001A CN105512662A CN 105512662 A CN105512662 A CN 105512662A CN 201510850001 A CN201510850001 A CN 201510850001A CN 105512662 A CN105512662 A CN 105512662A
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- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
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Abstract
The invention discloses a detection method and apparatus for an unlicensed vehicle. The method comprises: a vehicle in a video is detected and a vehicle sub image in the video is extracted; pretreatment is carried out on the detected sub image; image edge extraction is carried out on the sub image after the pretreatment; for an edge point, a sub image of a small area near the edge point is extracted and a color feature of the sub image is extracted; a vehicle color model is used for carrying out determination and whether the extracted sub image is a candidate license plate area is detected, all candidate license plate areas meeting the requirement are detected, and an area with the best matching effect among all candidate license plate areas is found and is determined as a license plate area; and if no license plate area is detected, the vehicle is determined to be an unlicensed vehicle. Therefore, a clear license plate can be identified and adaptability to the blurred license plate is also high, so that the license plate identification rate is improved and the effect of detection on the unlicensed vehicle is good.
Description
Technical field
The invention belongs to computer vision field, particularly relate to a kind of unlicensed vehicle checking method and device.
Background technology
Along with the development of society and the progress of science and technology, global car owning amount rapidly increases, a series of traffic problems are there are, bring immense pressure also to the traffic administration in city, intelligent transportation system is arisen at the historic moment under this background, and intelligent transportation system is realizing traffic administration intellectuality, robotization, simplification play a significantly greater role.
Current Car license recognition, in violation of rules and regulations detection, traffic statistics, traffic study are all the important component parts in intelligent transportation system.
Traffic study comprises the volume of traffic, the speed of a motor vehicle, vehicle statistics etc., and what pay close attention to here is the detection of unlicensed vehicle.Can add up the quantity of unlicensed vehicle on the one hand, can data analysis be carried out according to testing result thus track unlicensed car car owner on the other hand, more effectively carry out traffic administration.
Summary of the invention
The object of the present invention is to provide a kind of unlicensed vehicle checking method, the intellectuality of traffic administration, robotization and simplification can be realized by the method.
For achieving the above object, on the one hand, the invention provides a kind of unlicensed vehicle checking method, the method comprises the following steps:
Detect the vehicle in video, extract the vehicle subimage in video; Pre-service is carried out to the subimage detected; Edge extraction is carried out to pretreated subimage; For marginal point, extract pocket subimage near described marginal point, and extract the color characteristic of subimage; Use car plate color model to differentiate, whether the subimage of Detection and Extraction is candidate license plate region; Detect all qualified candidate license plate regions, and look for the region of mating most in all candidate license plate regions, be decided to be license plate area; If license plate area do not detected, then judge that vehicle is unlicensed vehicle.
Preferably, the vehicle in described detection video, the vehicle subimage step extracted in video comprises: detect the vehicle in video by auto model, extracts the vehicle subimage in video; Described auto model is Ha Er haar feature by extracting vehicle and then uses An Debusite adaboost sorter to carry out learning to obtain, and uses the vehicle in haar characteristic sum adaboost detection of classifier video when detection equally.
Haar feature is a kind of conventional feature interpretation operator that this area (i.e. computer vision field) technician knows.
Adaboost is a kind of iterative algorithm that those skilled in the art know, its core concept trains different sorters (Weak Classifier) for same training set, then these weak classifier set are got up, form a stronger final sorter (strong classifier).
Preferably, the described subimage to detecting carries out pre-treatment step and comprises: to the level and smooth of vehicle subimage and stretching, and then eliminate the impact of illumination and noise.
Preferably, describedly Edge extraction step is carried out to pretreated subimage comprise: adopt Sobel Sobel operator to carry out vertical rim detection to pretreated subimage.Sobel Sobel operator is a kind of important process method of computer vision field, is mainly used in the First-order Gradient obtaining digital picture, in the present invention for carrying out vertical rim detection to pretreated subimage.
Preferably, described for marginal point, extract pocket subimage near described marginal point, and the color characteristic step extracting subimage comprises: carry out binaryzation to pocket subimage, the color histogram of black region corresponding point and the color histogram of white portion corresponding point after statistics binaryzation respectively, the color space used is form and aspect intensity value hsv color space, the two is merged the color characteristic obtaining extracting block.The above-mentioned binary conversion treatment mentioned is that the gray-scale value of the pixel on pocket subimage is set to 0 or 255, namely extracts the black region in pocket subimage and white portion.
Preferably, described use car plate color model differentiates, whether the subimage of Detection and Extraction is that candidate license plate region step comprises: according to the car plate color model trained, judge whether current pocket subimage is license plate area.
Preferably, described color model is divided into four classes at training stage sample: wrongly written or mispronounced character car plate of the blue end, white gravoply, with black engraved characters car plate, black matrix wrongly written or mispronounced character car plate, yellow end surplus car plate, binaryzation is carried out to sample, after color space is transformed into HSV space by RGB, add up the histogram of the point that black and white region is corresponding after binaryzation respectively, and the two is merged the feature obtaining each sample, and then SVM classifier is used to train; At detection-phase, because support vector machines sorter can return a matching value, for each block region, if find that matching value is comparatively large through detecting, then think candidate license plate region, otherwise be not license plate area.
Preferably, the all qualified candidate license plate regions of described detection, and the region of mating most is looked in all candidate license plate regions, be decided to be license plate area step to comprise: by adjacent candidate license plate region merging technique, and the matching degree of car plate is judged by color model, think that the highest region of matching degree is real license plate area.
On the other hand, the invention provides a kind of unlicensed vehicle detection apparatus, this device comprises: the first detecting unit, processing unit, the first extraction unit, the second extraction unit, the second detecting unit and judging unit.Wherein,
First detecting unit, for detecting the vehicle in video, extracts the vehicle subimage in video;
Processing unit is used for carrying out pre-service to the subimage detected;
First extraction unit is used for carrying out Edge extraction to pretreated subimage;
Second extraction unit is used for for marginal point, extracts pocket subimage near described marginal point, and extracts the color characteristic of subimage;
Second detecting unit differentiates for using car plate color model, and whether the subimage of Detection and Extraction is candidate license plate region;
Judging unit for detecting all qualified candidate license plate regions, and looks for the region of mating most in all candidate license plate regions, is decided to be license plate area; If license plate area do not detected, then judge that vehicle is unlicensed vehicle.
The present invention uses auto model to detect vehicle, and then use color model to differentiate car plate, use color model to detect car plate and both can identify clear car plate, also can have good adaptability to fuzzy license plate, improve the discrimination of Car license recognition, and then have good effect to unlicensed car test survey.
Accompanying drawing explanation
The unlicensed vehicle checking method schematic process flow diagram of one that Fig. 1 provides for the embodiment of the present invention;
The unlicensed vehicle detection apparatus structural schematic block diagram of one that Fig. 2 provides for the embodiment of the present invention.
Embodiment
After being described in detail embodiments of the present invention by way of example below in conjunction with accompanying drawing, other features of the present invention, feature and advantage will be more obvious.
The unlicensed vehicle checking method schematic process flow diagram of one that Fig. 1 provides for the embodiment of the present invention.As shown in Figure 1, the method comprising the steps of 101-107:
Step 101, detects the vehicle in video, extracts the vehicle subimage in video.
Use the vehicle in auto model detection video, the training stage at auto model: arrange a large amount of positive sample vehicle images and negative sample non-vehicle image, extract the haar feature of sample, train in conjunction with adaboost sorter, obtain two class discrimination models.At detection-phase, use same characteristic sum sorter and the model that trains to carry out the detection of vehicle, extract the vehicle subimage in video.
Step 102, carries out pre-service to the subimage detected.
After acquisition vehicle subimage, need to detect car plate in subimage, the image that can't detect car plate thinks unlicensed car.First pre-service is carried out to the subimage detected, comprise the level and smooth of image and stretch, thus the impact of stress release treatment and illumination etc.This step is mainly in order to follow-up rim detection is prepared.
Step 103, carries out Edge extraction to pretreated subimage.
Because license plate area edge is obvious, vertical rim detection is carried out to pretreated image.Preferably, Sobel operator can be adopted to detect.
Step 104, for marginal point, extracts pocket subimage near described marginal point, and extracts the color characteristic of subimage.
Binaryzation is carried out to pocket subimage, is about to be set to 0 or 255 to the gray-scale value of the pixel of pocket subimage, namely whole image is presented and significantly only have black and white visual effect.The color histogram of black region corresponding point and the color histogram of white portion corresponding point after statistics binaryzation respectively, the color space of use is hsv color space, the two is merged the color characteristic obtaining extracting block.Binarization methods makes color characteristic more have distinction.
Step 105, use car plate color model to differentiate, whether the subimage of Detection and Extraction is candidate license plate region.
After the color characteristic extracting subimage, whether use car plate color model to differentiate, detecting is candidate license plate region.Here color model is divided into four classes at training stage sample: wrongly written or mispronounced character car plate of the blue end, white gravoply, with black engraved characters car plate, black matrix wrongly written or mispronounced character car plate, yellow end surplus car plate, binaryzation is carried out to sample, after color space is transformed into HSV space by RGB, add up the histogram of the point that black and white region is corresponding after binaryzation respectively, and the two is merged the feature obtaining each sample, and then SVM classifier is used to train.At detection-phase, because SVM classifier can return a matching value, for each block region, if find that matching value is comparatively large through detecting, then think candidate license plate region, otherwise be not license plate area.
Step 106, detects all qualified candidate license plate regions, and look for the region of mating most in all candidate license plate region, be decided to be license plate area;
By adjacent candidate license plate region merging technique, and judged the matching degree of car plate by color model, think that the highest region of matching degree is real license plate area.
Step 107, if license plate area do not detected, then judges that vehicle is unlicensed vehicle.
In embodiments of the present invention, because license plate area is that piecemeal detects, the license plate area border found is inaccurate, uses colouring information, four borders up and down of accurate positioning licence plate.
The present invention detects vehicle by using auto model, and then use color model to differentiate car plate, use color model to detect car plate and both can identify clear car plate, also can have good adaptability to fuzzy license plate, improve the discrimination of Car license recognition, and then have good effect to unlicensed car test survey.
The unlicensed vehicle detection apparatus structural schematic block diagram of one that Fig. 2 provides for the embodiment of the present invention.As shown in Figure 2, this device comprises: the first detecting unit, processing unit, the first extraction unit, the second extraction unit, the second detecting unit and judging unit.Wherein,
First detecting unit, for detecting the vehicle in video, extracts the vehicle subimage in video;
Processing unit is used for carrying out pre-service to the subimage detected;
First extraction unit is used for carrying out Edge extraction to pretreated subimage;
Second extraction unit is used for for marginal point, extracts pocket subimage near described marginal point, and extracts the color characteristic of subimage;
Second detecting unit differentiates for using car plate color model, and whether the subimage of Detection and Extraction is candidate license plate region;
Judging unit for detecting all qualified candidate license plate regions, and looks for the region of mating most in all candidate license plate regions, is decided to be license plate area; If license plate area do not detected, then judge that vehicle is unlicensed vehicle.
The present invention detects vehicle by using auto model, and then use color model to differentiate car plate, use color model to detect car plate and both can identify clear car plate, also can have good adaptability to fuzzy license plate, improve the discrimination of Car license recognition, and then have good effect to unlicensed car test survey.
Obviously, under the prerequisite not departing from true spirit of the present invention and scope, the present invention described here can have many changes.Therefore, all changes that it will be apparent to those skilled in the art that, all should be included within scope that these claims contain.The present invention's scope required for protection is only limited by described claims.
Claims (10)
1. a unlicensed vehicle checking method, is characterized in that, comprises the following steps:
Detect the vehicle in video, extract the vehicle subimage in video;
Pre-service is carried out to the subimage detected;
Edge extraction is carried out to pretreated subimage;
For marginal point, extract pocket subimage near described marginal point, and extract the color characteristic of subimage;
Use car plate color model to differentiate, whether the subimage of Detection and Extraction is candidate license plate region;
Detect all qualified candidate license plate regions, and look for the region of mating most in all candidate license plate regions, be decided to be license plate area;
If license plate area do not detected, then judge that vehicle is unlicensed vehicle.
2. method according to claim 1, is characterized in that, the vehicle in described detection video, and the vehicle subimage step extracted in video comprises:
Detect the vehicle in video by auto model, extract the vehicle subimage in video; Described auto model is Ha Er haar feature by extracting vehicle and then uses An Debusite adaboost sorter to carry out learning to obtain, and uses the vehicle in haar characteristic sum adaboost detection of classifier video when detection equally.
3. method according to claim 1, is characterized in that, the described subimage to detecting carries out pre-treatment step and comprises:
To the level and smooth of vehicle subimage and stretch processing, and then eliminate the impact of illumination and noise.
4. method according to claim 1, is characterized in that, describedly carries out Edge extraction step to pretreated subimage and comprises:
Sobel Sobel operator is adopted to carry out vertical rim detection to pretreated subimage.
5. method according to claim 1, is characterized in that, described for marginal point, extracts pocket subimage near described marginal point, and the color characteristic step extracting subimage comprises:
Binaryzation is carried out to pocket subimage, the color histogram of black region corresponding point and the color histogram of white portion corresponding point after statistics binaryzation respectively, the color space used is form and aspect intensity value hsv color space, the two is merged the color characteristic obtaining extracting block.
6. method according to claim 1, is characterized in that, described use car plate color model differentiates, whether the subimage of Detection and Extraction is that candidate license plate region step comprises:
According to the car plate color model trained, judge whether current pocket subimage is license plate area.
7. method according to claim 6, it is characterized in that, described color model is divided into four classes at training stage sample: wrongly written or mispronounced character car plate of the blue end, white gravoply, with black engraved characters car plate, black matrix wrongly written or mispronounced character car plate, yellow end surplus car plate, binaryzation is carried out to sample, after color space is transformed into HSV space by RGB, add up the histogram of the point that black and white region is corresponding after binaryzation respectively, and the two is merged the feature obtaining each sample, and then use support vector machines sorter to train; At detection-phase, because SVM classifier can return a matching value, for each block region, if find that matching value is comparatively large through detecting, then think candidate license plate region, otherwise be not candidate license plate region.
8. method according to claim 1, is characterized in that, all qualified candidate license plate regions of described detection, and looks for the region of mating most in all candidate license plate regions, is decided to be license plate area step and comprises:
By adjacent candidate license plate region merging technique, and judged the matching degree of car plate by color model, think that the highest region of matching degree is real license plate area.
9. a unlicensed vehicle detection apparatus, is characterized in that, comprising:
First detecting unit, for detecting the vehicle in video, extracts the vehicle subimage in video;
Processing unit, for carrying out pre-service to the subimage detected;
First extraction unit, for carrying out Edge extraction to pretreated subimage;
Second extraction unit, for for marginal point, extracts pocket subimage near described marginal point, and extracts the color characteristic of subimage;
Second detecting unit, for using car plate color model to differentiate, whether the subimage of Detection and Extraction is candidate license plate region;
Judging unit, for detecting all qualified candidate license plate regions, and looking for the region of mating most, being decided to be license plate area in all candidate license plate regions; If license plate area do not detected, then judge that vehicle is unlicensed vehicle.
10. device according to claim 9, is characterized in that, described first detecting unit specifically for:
Detect the vehicle in video by auto model, extract the vehicle subimage in video; Described auto model is haar feature by extracting vehicle and then uses adaboost sorter to carry out learning to obtain, and uses the vehicle in haar characteristic sum adaboost detection of classifier video when detection equally.
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| CN106845341A (en) * | 2016-12-15 | 2017-06-13 | 南京积图网络科技有限公司 | A kind of unlicensed vehicle identification method based on virtual number plate |
| CN107918941A (en) * | 2017-11-01 | 2018-04-17 | 国网山东省电力公司电力科学研究院 | A kind of visualizing monitor system and method for broken protection outside passway for transmitting electricity |
| CN107977596A (en) * | 2016-10-25 | 2018-05-01 | 杭州海康威视数字技术股份有限公司 | A kind of car plate state identification method and device |
| CN113158758A (en) * | 2021-02-07 | 2021-07-23 | 中国联合网络通信集团有限公司 | Method, system, equipment and storage medium for judging illegal use of vehicle license plate |
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Application publication date: 20160420 |