CN104361366B - A kind of licence plate recognition method and car license recognition equipment - Google Patents
A kind of licence plate recognition method and car license recognition equipment Download PDFInfo
- Publication number
- CN104361366B CN104361366B CN201410742690.8A CN201410742690A CN104361366B CN 104361366 B CN104361366 B CN 104361366B CN 201410742690 A CN201410742690 A CN 201410742690A CN 104361366 B CN104361366 B CN 104361366B
- Authority
- CN
- China
- Prior art keywords
- car
- negative sample
- scene
- car plate
- training set
- 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.)
- Expired - Fee Related
Links
- 238000000034 method Methods 0.000 title claims abstract description 61
- 230000003068 static effect Effects 0.000 claims abstract description 151
- 238000001514 detection method Methods 0.000 claims abstract description 100
- 239000013598 vector Substances 0.000 claims description 33
- 108010001267 Protein Subunits Proteins 0.000 claims description 8
- 238000012706 support-vector machine Methods 0.000 claims description 7
- 239000012141 concentrate Substances 0.000 claims description 2
- 239000000284 extract Substances 0.000 claims 1
- 238000010586 diagram Methods 0.000 description 8
- 238000007689 inspection Methods 0.000 description 6
- 238000005516 engineering process Methods 0.000 description 5
- 238000012512 characterization method Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 4
- 230000011218 segmentation Effects 0.000 description 3
- 238000006073 displacement reaction Methods 0.000 description 2
- 241000208340 Araliaceae Species 0.000 description 1
- 235000005035 Panax pseudoginseng ssp. pseudoginseng Nutrition 0.000 description 1
- 235000003140 Panax quinquefolius Nutrition 0.000 description 1
- 230000003044 adaptive effect Effects 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000002068 genetic effect Effects 0.000 description 1
- 235000008434 ginseng Nutrition 0.000 description 1
- 238000012804 iterative process Methods 0.000 description 1
- 230000007257 malfunction Effects 0.000 description 1
- 210000000056 organ Anatomy 0.000 description 1
- 230000000750 progressive effect Effects 0.000 description 1
- 230000000717 retained effect Effects 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 238000009423 ventilation Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
- G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
- G06F18/2155—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/285—Selection of pattern recognition techniques, e.g. of classifiers in a multi-classifier system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/625—License plates
Landscapes
- Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Evolutionary Biology (AREA)
- Evolutionary Computation (AREA)
- Physics & Mathematics (AREA)
- General Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Life Sciences & Earth Sciences (AREA)
- Traffic Control Systems (AREA)
- Image Analysis (AREA)
- Character Discrimination (AREA)
Abstract
This application discloses a kind of licence plate recognition method and car license recognition equipments, according to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；Static negative sample is added in the original training set of car license recognition equipment, the first training set is obtained, the first car plate grader is trained according to the first training set；According to the car scene at the first car plate detection of classifier scene, the dynamic negative sample for collecting scene, dynamic negative sample is added in the first training set, the second training set is obtained, the second car plate grader is trained according to the second training set, and Car license recognition is carried out according to the second car plate grader.In this way, the the second car plate grader trained using the second training set can remove static negative sample and dynamic negative sample, most negative samples that scene is likely to occur at this time can be refused by the second car plate grader, accurately identified to car plate to reach, reduce the false drop rate of car license recognition equipment.
Description
Technical field
This application involves image identification technical fields, more particularly to a kind of licence plate recognition method and car license recognition equipment.
Background technology
In order to ensure good traffic order or social security, the products such as car license recognition equipment based on license plate recognition technology
It is widely used in the positions such as intersection, property plots, business premises and government organs.License plate recognition technology is generally divided into vehicle
Board detection, Character segmentation identification and car plate are voted this three big step.Car plate detection, i.e., from video detect car plate and determine its
Position in each frame image is a step that is more crucial in license plate recognition technology and taking.
Currently, car license recognition equipment is trained in laboratory.Under normal conditions, there are one vehicle frontals
Car plate, and in crossing, sentry box or bayonet, vehicle is all current successively, by using video camera in each period and weather
Under the conditions of shoot and store multitude of video at various locations, in the image of these videos, manually intercept out the car plate of each car
Image manually intercepts out non-license plate image therein as negative sample, then trains to obtain by positive and negative samples as positive sample
Car license recognition equipment.
However, in practical applications, site environment and applicable cases are ever-changing, and car license recognition equipment is in laboratory training
When due to the value volume and range of product of negative sample it is limited, all application scenarios and working hour can not be taken into account, thus Car license recognition is set
There can be certain flase drop when standby use at the scene, i.e., some non-license plate images are mistakenly identified as car plate.Flase drop packet
Include static flase drop and dynamic flase drop.Static flase drop refer to scene scene in some backgrounds, such as roadside fence, extensively
The telephone number etc. on board is accused, these background patterns and actual car plate are much like, and car license recognition equipment is easy to these to carry on the back
Scape is mistakenly identified as car plate, for example, the fence in roadside and " 111111 " it is much like, be easily identified into " province L11111 ", separately
Outer some characters such as " 1 ", " L ", " T " are also easy to be wrongly recognized into " H " or " Y ".Dynamically flase drop refers to mobile object
Advertisement etc. on body, such as the ventilation opening of car engine, vehicle body, these objects will appear once in a while, due to comparing similar car plate,
Certain flase drop can be caused, if car license recognition equipment is applied in express company's entrance, the places such as public transport company parking lot,
Due to all having advertisement and telephone number etc. on a large amount of vehicle body, it is easy for a large amount of flase drops occur, Car license recognition is caused to malfunction.
Invention content
In view of this, a kind of licence plate recognition method of the application offer and car license recognition equipment, to realize to the accurate of car plate
Identification, reduces the false drop rate of car license recognition equipment.
To achieve the goals above, technical solution provided by the embodiments of the present application is as follows：
A kind of licence plate recognition method is applied in the car license recognition equipment of arrangement at the scene, in the car license recognition equipment
Include the original car plate detection classifier trained according to original training set, the licence plate recognition method includes：
According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；It is described quiet
State negative sample be the scene no car scene in by background image that the original car plate detection classifier flase drop is license plate area；
The static negative sample is added in the original training set of the car license recognition equipment, the first training is obtained
Collection trains the first car plate grader according to first training set；
According to the car scene at the first car plate detection of classifier scene, the dynamic negative sample at the scene of collecting will be described
Dynamic negative sample is added in first training set, obtains the second training set, and the second vehicle is trained according to second training set
Board grader, and Car license recognition is carried out according to the second car plate grader；The dynamic negative sample has vehicle for the scene
In scene by the first car plate grader flase drop be license plate area moving image.
Preferably, described that first car plate grader is trained according to first training set, including：
Each positive sample in first training set is characterized using Haar features, it is special to form positive sample Haar
Sign vector；
Each negative sample in first training set is characterized using Haar features, it is special to form negative sample Haar
Sign vector；
The positive sample Haar characteristic vector and the negative sample Haar characteristic vectors are carried out using Adaboost algorithm
Training obtains the first car plate grader.
Preferably, the dynamic of the car scene according to the first car plate detection of classifier scene, the scene of collecting is negative
Sample, including：
The video image that the car scene at car license recognition equipment detection scene obtains is obtained, the video image is extracted
In suspected license plate area, according to the license plate area in suspected license plate area described in the first car plate detection of classifier；
Multiple characters are partitioned into from the license plate area, the Recognition of License Plate Characters mould trained according to support vector machines
Multiple characters are identified in type, and judge the recognition confidence of each character；
Judge whether the license plate area is effective according to the recognition confidence of each character, if in vain, it will be described
Dynamic negative sample of the license plate area as the scene.
Preferably, the recognition confidence of each character of the basis judges whether the license plate area is effective, including：
Judge whether the number of the character is 7；
If so, judging whether the recognition confidence of each character is greater than or equal to first threshold；
If so, judge the recognition confidence of 7 characters and whether be greater than or equal to second threshold；
If it is, judging that the license plate area is effective, otherwise in vain.
Preferably, described that the dynamic negative sample is added in first training set, including：
The false drop rate of the car scene at the first car plate detection of classifier scene is counted according to preset time interval, and
Judge whether the false drop rate is more than third threshold value；
If so, judging whether the number for the dynamic negative sample being collected into is greater than or equal to the 4th threshold value；
If so, the dynamic negative sample is added in first training set.
Preferably, in the no car scene according to the original car plate detection classifier detection scene, the static state at the scene of collecting
After negative sample, further include：
Identify simple static negative sample in the static negative sample and difficult static negative sample, and by the simple static
Negative sample is added in the original training set of the car license recognition equipment；
Whether the image-region where judging the difficult static negative sample meets shielding requirements；
If it is satisfied, the image-region where the shielding difficult static negative sample, if conditions are not met, according to the difficulty
Static negative sample trains third car plate grader, and the third car plate grader is for judging the second car plate grader identification
Whether the car plate gone out is effective car plate.
Preferably, in the no car scene according to the original car plate detection classifier detection scene, the static state at the scene of collecting
After negative sample, further include：
Identify simple static negative sample in the static negative sample and difficult static negative sample, and by the simple static
Negative sample is added in the original training set of the car license recognition equipment；
Whether the image-region where judging the difficult static negative sample meets shielding requirements；
If it is satisfied, the image-region where the shielding difficult static negative sample, if conditions are not met, in the preset time
Whether the image-region where judging the difficult static negative sample in section moves, will be described if do not moved
Difficult static negative sample is added in the original training set of the car license recognition equipment.
The application also provides a kind of car license recognition equipment, and Car license recognition, the Car license recognition are carried out at the scene for arranging
Equipment includes the original car plate detection classifier trained according to original training set, and the car license recognition equipment further includes：
Static negative sample collection module is collected for the no car scene according to the original car plate detection classifier detection scene
The static negative sample at scene；The static negative sample be the scene no car scene in by the original car plate detection classifier flase drop
For the background image of license plate area；
First car plate classifier modules, the original for the static negative sample to be added to the car license recognition equipment
In beginning training set, the first training set is obtained, the first car plate grader is trained according to first training set；
Second car plate classifier modules are collected for the car scene according to the first car plate detection of classifier scene
The dynamic negative sample at scene, the dynamic negative sample is added in first training set, the second training set is obtained, according to institute
It states the second training set and trains the second car plate grader, and Car license recognition is carried out according to the second car plate grader；The dynamic
Negative sample be the scene car scene in by moving image that the first car plate grader flase drop is license plate area.
Preferably, the first car plate classifier modules, including：
Positive sample feature vector units, for using Haar features to each positive sample in first training set into
Row characterization, forms positive sample Haar characteristic vector；
Negative sample feature vector units, for using Haar features to each negative sample in first training set into
Row characterization, forms negative sample Haar characteristic vectors；
Training unit, for utilizing Adaboost algorithm to the positive sample Haar characteristic vector and the negative sample Haar
Feature vector is trained, and obtains the first car plate grader.
Preferably, the second car plate classifier modules, including：
Detection unit, the video image that the car scene for obtaining car license recognition equipment detection scene obtains, carries
The suspected license plate area in the video image is taken, according in suspected license plate area described in the first car plate detection of classifier
License plate area；
Recognition unit, for being partitioned into multiple characters from the license plate area, according to the vehicle of support vector machines training
Multiple characters are identified in board character recognition model, and judge the recognition confidence of each character；
Dynamic negative sample unit, for judging whether the license plate area has according to the recognition confidence of each character
Effect, if in vain, using the license plate area as the dynamic negative sample at the scene.
Preferably, the dynamic negative sample unit, including：
First judgment sub-unit, for judging whether the number of the character is 7；
Second judgment sub-unit, for if so, judging whether the recognition confidence of each character is greater than or equal to
First threshold；
Third judgment sub-unit, for if so, judge the recognition confidence of 7 characters and whether be more than or wait
In second threshold；
Subelement is judged, for if it is, judging that the license plate area is effective, otherwise in vain.
Preferably, the second car plate classifier modules, including：
Statistic unit has parking lot for count the first car plate detection of classifier scene according to preset time interval
The false drop rate of scape, and judge whether the false drop rate is more than third threshold value；
Judging unit, for if so, judging whether the number of the dynamic negative sample being collected into is greater than or equal to the
Four threshold values；
Adding device, for if so, the dynamic negative sample is added in first training set.
Preferably, the static negative sample collection module, including：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and
The simple static negative sample is added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if discontented
Foot trains third car plate grader according to the difficult static negative sample, and the third car plate grader is for judging described the
Whether the car plate that two car plate graders identify is effective car plate.
Preferably, the static negative sample collection module, including：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and
The simple static negative sample is added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if discontented
Foot, whether the image-region where judging the difficult static negative sample within the preset period moves, if do not sent out
The difficult static negative sample is then added in the original training set of the car license recognition equipment by raw movement.
By the above technical solution provided by the present application, licence plate recognition method is applied to the car license recognition equipment of arrangement at the scene
In, the car license recognition equipment includes the original car plate detection classifier trained according to original training set, the licence plate recognition method
According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；The static negative sample
For in the no car scene at the scene by background image that the original car plate detection classifier flase drop is license plate area；By the static state
Negative sample is added in the original training set of the car license recognition equipment, obtains the first training set, is instructed according to described first
Practice collection the first car plate grader of training；According to the car scene at the first car plate detection of classifier scene, the scene of collecting is moved
The dynamic negative sample is added in first training set, obtains the second training set by state negative sample, is instructed according to described second
Practice collection the second car plate grader of training, and Car license recognition is carried out according to the second car plate grader；The dynamic negative sample is
In the car scene at the scene by the first car plate grader flase drop be license plate area moving image.In this way, first collecting
The static negative sample at scene is simultaneously added to original training set, obtains the first training set, and the first car plate is trained using the first training set
Grader removes static negative sample, then reuses the first car plate grader and collects dynamic negative sample and be added to the first training
Collection, obtains the second training set, trains the second car plate grader that can remove the static state being collected into using the second training set and bears sample
Originally the most negative samples being likely to occur with dynamic negative sample, the scene of having been contained in the second training set at this time, these are negative
Sample can be refused by the second car plate grader, accurately identified to car plate to reach, reduce the flase drop of car license recognition equipment
Rate.
Description of the drawings
In order to illustrate the technical solutions in the embodiments of the present application or in the prior art more clearly, to embodiment or will show below
There is attached drawing needed in technology description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments described in application, for those of ordinary skill in the art, without creative efforts,
Other drawings may also be obtained based on these drawings.
Fig. 1 is a kind of flow diagram of licence plate recognition method provided by the present application；
Fig. 2 is the flow diagram of another licence plate recognition method provided by the present application；
Fig. 3 is the flow diagram of another licence plate recognition method provided by the present application；
Fig. 4 is a kind of structural schematic diagram of car license recognition equipment provided by the present application.
Specific implementation mode
In order to make those skilled in the art better understand the technical solutions in the application, below in conjunction with attached drawing, it is right
The technical solution of the application is clearly and completely described, it is clear that described embodiment is only that the application part is implemented
Example, instead of all the embodiments.Based on the embodiment in the application, those of ordinary skill in the art are not making creativeness
The every other embodiment obtained under the premise of labour, shall fall within the protection scope of the present application.
Below in conjunction with the accompanying drawings, the embodiment of the application is described in detail.
Fig. 1 is a kind of flow diagram of licence plate recognition method provided by the present application.
It please refers to shown in Fig. 1, the embodiment of the present application provides a kind of licence plate recognition method, is applied to the car plate of arrangement at the scene
In identification equipment, the car license recognition equipment includes the original car plate detection classifier trained according to original training set, the car plate
Recognition methods includes：
S100：According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；
The static negative sample be the scene no car scene in by the original car plate detection classifier flase drop be car plate area
The background image in domain；
In the embodiment of the present application, it after car license recognition equipment being installed at the scene, is detected first using the original car plate detection classifier
Live no car scene collects the image for leading to the original car plate detection classifier flase drop, divides negative sample, i.e. static negative sample as mistake.
When car license recognition equipment is installed at the scene, the original positive sample and original of laboratory training car plate grader
Beginning negative sample is stored in the computer at scene.Open Car license recognition device after, ensure scene without vehicle in the case of according to
The varying environments such as daytime, night, cloudy day, fine day, Qiang Guang, dim light carry out video flowing car plate detection to scene, if can detect
Car plate means that certain backgrounds in field scene are identified as car plate, retains the sample of these mistakes at this time, i.e., live
Static negative sample.
S200：The static negative sample is added in the original training set of the car license recognition equipment, obtains
One training set trains the first car plate grader according to first training set；
In the embodiment of the present application, the static negative sample at scene is added to original training set, obtains the first training set, root
New car plate grader i.e. the first car plate grader is trained according to new training set i.e. the first training set, to use the first new car plate
Grader substitutes old the original car plate detection classifier and carries out car plate detection, so that it may not influenced by static negative sample, realize to quiet
The correct identification of state negative sample.
S300：According to the car scene at the first car plate detection of classifier scene, the dynamic negative sample at the scene of collecting will
The dynamic negative sample is added in first training set, obtains the second training set, according to second training set training the
Two car plate graders, and Car license recognition is carried out according to the second car plate grader.
The dynamic negative sample be the scene car scene in by the first car plate grader flase drop be car plate area
The moving image in domain.
In the embodiment of the present application, after substituting old the original car plate detection classifier with the first new car plate grader, first
The dynamic negative sample at scene is collected during the use of car plate grader, and the dynamic negative sample of collection is added to the first training
It concentrates, obtains the second training set, the second car plate grader is trained according to the second training set again, and according to the second car plate grader
Carry out Car license recognition, so that it may not influenced by the static negative sample at scene and the dynamic negative sample being collected into, realization pair
The correct identification of static negative sample and the dynamic negative sample being collected into reduces false drop rate.
Further, during the use of the second car plate grader, the application can also continue to collect dynamic negative sample,
And continue to be added in the second training set by the dynamic negative sample of collection and obtain third training set, and trained and assembled for training according to third
Practice third car plate grader, Car license recognition is carried out according to third car plate grader, and so on.This method is not necessarily to manual intervention,
The flase drop of car license recognition equipment can be effectively reduced after successive ignition, subsequent alternative manner is similar with step 300, belongs to same
The repetition of one principle is used, and details are not described herein again.
By the above technical solution provided by the present application, licence plate recognition method is applied to the car license recognition equipment of arrangement at the scene
In, the car license recognition equipment includes the original car plate detection classifier trained according to original training set, the licence plate recognition method
According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；The static negative sample
For in the no car scene at the scene by background image that the original car plate detection classifier flase drop is license plate area；By the static state
Negative sample is added in the original training set of the car license recognition equipment, obtains the first training set, is instructed according to described first
Practice collection the first car plate grader of training；According to the car scene at the first car plate detection of classifier scene, the scene of collecting is moved
The dynamic negative sample is added in first training set, obtains the second training set by state negative sample, is instructed according to described second
Practice collection the second car plate grader of training, and Car license recognition is carried out according to the second car plate grader；The dynamic negative sample is
In the car scene at the scene by the first car plate grader flase drop be license plate area moving image.In this way, first collecting
The static negative sample at scene is simultaneously added to original training set, obtains the first training set, and the first car plate is trained using the first training set
Grader removes static negative sample, then reuses the first car plate grader and collects dynamic negative sample and be added to the first training
Collection, obtains the second training set, trains the second car plate grader that can remove the static state being collected into using the second training set and bears sample
Originally the most negative samples being likely to occur with dynamic negative sample, the scene of having been contained in the second training set at this time, these are negative
Sample can be refused by the second car plate grader, accurately identified to car plate to reach, reduce the flase drop of car license recognition equipment
Rate.
Above mentioned embodiment provide a kind of licence plate recognition methods, wherein trains the first car plate according to first training set
The method of grader, the present embodiment will be described with reference to the drawings：
Fig. 2 is the flow diagram of another licence plate recognition method provided by the present application.
It please refers to shown in Fig. 2, method provided by the embodiments of the present application, including：
S201：Each positive sample in first training set is characterized using Haar features, forms positive sample
Haar feature vectors；
S202：Each negative sample in first training set is characterized using Haar features, forms negative sample
Haar feature vectors；
S203：Using Adaboost algorithm to the positive sample Haar characteristic vector and the negative sample Haar characteristic vectors
It is trained, obtains the first car plate grader.
Original training set includes original positive sample and original negative sample, and static negative sample is added in original training set
Afterwards, equally include positive sample and negative sample in the first training set obtained, negative sample therein refers to original negative sample and static state
The sum of negative sample.
Detection method of license plate based on video have include much in the binary image based on line template angle detection calculate
Method detects car plate etc. using genetic algorithm.
In the embodiment of the present application, it is preferred to use the Adaboost algorithm based on Haar features trains the classification of the first car plate
Device characterizes the positive and negative samples of each width car plate using Haar features, forms Haar feature vectors.Finally using cascade
Adaboost algorithm Haar features are trained, obtain the first car plate grader.
Haar features are a kind of rectangular characteristics, and rectangular characteristic compares some simple graphic structure such as edges, line segment
Sensitivity, but particular orientation can only be described, therefore it is relatively coarse.But for a detector, the inside includes that hundreds of thousands is different
Rectangular characteristic, then be trained by using Adaboost algorithm, so that it may to obtain a strong classifier, i.e. the first car plate point
Class device.
The feature templates of each Haar be by two or more congruences rectangle it is adjacent be composed, have in feature templates
White and two kinds of rectangles of black, and by this template definition be white rectangle pixel and subtract black rectangle pixel and.Feature templates
It can arbitrarily be placed with arbitrary dimension in child window, each form is known as a feature, finds out all features of child window,
It is the basis for carrying out weak typing training.
It can accelerate the calculating of Haar features using integrogram.In order to avoid the marginal value of all the points of a box is added
Compute repeatedly, used integrogram in the algorithm.Each point (x, y) on integrogram contains from point (0,0) to point (x, y)
The marginal value of all pixels.The ash of all black picture elements in a rectangular characteristic can be quickly obtained by using integrogram
Angle value and and all white pixels the sum of gray value, the subtraction that then tries again obtains a Haar feature
Value.
Adaboost algorithm is a kind of adaptive boosting algorithms, and basic thought is when grader is to certain samples
When correct classification, then the weights of these samples are reduced.When mistake is classified, then increases the weights of these samples, allow learning algorithm
It is concentrated in subsequent study and more difficult training sample is learnt, finally obtained a recognition accuracy and preferably classify
Device.Each layer of training using minimum allowable detection rate and maximum allowable false positive rate as strong classifier iteration stopping foundation, when
Each layer of strong classifier and all reach training before setting value when, the grade training i.e. complete.The instruction of next layer of strong classifier
Practicing negative sample will be generated from this layer in the negative sample of mistake classification.Selection needs the type of Haar features to be used, is loaded into
Positive sample and negative sample.False alarm rate is set, and the number of plies of grader can start to train.Each layer of training finishes in training process
It can test and see whether had reached false alarm rate, if reached, training terminates.Otherwise it is trained always until having reached needs
The trained number of plies.
Adaboost training flow be：A series of training sample is given, the weight of each sample is initialized, weight is returned
One turns to a probability distribution, and a Weak Classifier is trained to each Haar features, calculates the Weak Classifier of corresponding all features
Weighted sum error rate, choose and possess the best Weak Classifier of minimal error rate.
(feature f) is exactly to determine the optimal threshold of f in the case where present weight is distributed, make to one Weak Classifier of training
Obtaining this Weak Classifier, (feature f) is minimum to the error in classification of all training samples.It is exactly to select to choose a best Weak Classifier
That is selected to the error in classification of all training samples that Weak Classifier (feature) minimum in all Weak Classifiers.
When first car plate grader treats a width image to be detected, it is equivalent to and all Weak Classifiers is allowed to vote, it is weak point each
The weight of class device is all different, then to voting results according to error rates of weak classifiers weighted sum, threshold value table if more than
Show that current sample has passed through the detection of the first car plate grader.The false drop rate of first car plate grader is with Weak Classifier quantity
Increase and is reduced rapidly with the reduction of Weak Classifier false drop rate.
In the embodiment of the present application, the static negative sample at scene is added to original training set, according to the first obtained instruction
Practice the first new car plate grader of collection training, training flow can be：
1, each positive sample in first training set is characterized using Haar features, forms positive sample Haar
Feature vector.
2, each negative sample in first training set is characterized using Haar features, forms negative sample Haar
Feature vector.
3, the positive sample Haar characteristic vector and the negative sample obtained in Adaboost algorithm pair 1 and 2 is utilized
Haar feature vectors are trained, and obtain the first car plate grader.
The present invention is used carries out car plate by the first car plate grader based on Haar features that Adaboost algorithm is trained
Detection.It was verified that there is higher inspection by the first car plate grader based on Haar features that Adaboost algorithm is trained
Survey rate and lower false drop rate, and integrogram is coordinated to use, also there is no problem for real-time.
Further, on the basis of the above embodiments, parking lot is had according to the first car plate detection of classifier scene
Scape, the method for the dynamic negative sample at the scene of collecting, the present embodiment will be described with reference to the drawings：
Fig. 3 is the flow diagram of another licence plate recognition method provided by the present application.
It please refers to shown in Fig. 3, method provided by the embodiments of the present application, including：
301：The video image that the car scene at car license recognition equipment detection scene obtains is obtained, the video is extracted
Suspected license plate area in image, according to the license plate area in suspected license plate area described in the first car plate detection of classifier；
The licence plate recognition method that the application proposes is the car plate detection strategy that Rough Inspection adds essence to examine, that is, first passes through a series of sides
Method finds the doubtful region for including car plate, then reuses first based on Haar features trained by Adaboost algorithm
Car plate grader is detected the doubtful suspected license plate area comprising car plate, finds license plate area.There are many method of Rough Inspection, example
The edge of full figure is such as extracted, the big region of marginal density is found.Or find full figure marking area etc..The details of Rough Inspection is not at this
Invention discusses range.
302：It is partitioned into multiple characters from the license plate area, is known according to the characters on license plate of support vector machines training
Multiple characters are identified in other model, and judge the recognition confidence of each character；
303：Judge whether the license plate area is effective according to the recognition confidence of each character, if in vain, it will
Dynamic negative sample of the license plate area as the scene.
In actual use, it doubtful is examined using the first car plate grader comprising license plate area to what Rough Inspection obtained
It surveys, the license plate area then obtained to detection is split and identifies, if the result of segmentation and identification cannot meet effective vehicle
The requirement of board, then using the sample as dynamic negative sample.
Since car plate grader is there are certain missing inspection and flase drop, the result of detection may be 0,1 or more
A license plate area.The license plate area detected is split and is identified, character recognition of the invention, which uses, passes through support
Vector machine trains the Recognition of License Plate Characters model come, and then single character is identified using the model.It is each identified
Character out all contains there are one confidence level, finally according to the judgement of the result of segmentation and identification detect whether target effective：
1, car plate must include 7 characters；
2, the confidence level of each character must reach respective threshold value (preset first threshold)；
3, the summation that the confidence level of 7 characters adds up must also reach a threshold value (preset second threshold).
The license plate area for meeting three above condition is determined as effective car plate, if the license plate area that detected cannot
Meet three above condition, is just judged as invalid car plate, i.e. dynamic negative sample.
In addition, on the basis of the above method provided by the embodiments of the present application, dynamic negative sample is being added to the first instruction
When practicing concentration, may include：
The false drop rate of the car scene at the first car plate detection of classifier scene is counted according to preset time interval, and
Judge whether the false drop rate is more than preset third threshold value；If so, judging the number for the dynamic negative sample being collected into
Whether preset 4th threshold value is greater than or equal to；If so, the dynamic negative sample is added in first training set.
In the embodiment of the present application, car license recognition equipment can also check the flase drop of current system at a certain time interval
Rate.If false drop rate is more than preset third threshold value, it is (preset to check whether the negative sample number being collected into meets training requirement
4th threshold value), if conditions are not met, then continuing to collect dynamic negative sample, if enough dynamic negative samples are collected into, collection
Dynamic negative sample be added in the first training set, the second car plate grader of training and can continue iteration, with newly trained vehicle
Board grader replaces old car plate grader, to further decrease false drop rate.
It is emphasized that in the embodiment of the present application, the second training set for using of the second car plate grader of training and after
Training set in continuous iterative process includes the original negative sample that the original car plate detection classifier in laboratory uses, live static state always
Negative sample and the dynamic negative sample at scene.
The training that can be seen that car plate grader from the above flow can be the process of an iteration, when having reached again
A new car plate grader can be trained when trained condition.If at a certain time interval, Car license recognition
Effect it is bad, when reached meet training condition when, can continue to train next time, process is trained several times
Afterwards, can obtain that an effect is fine, the new car plate grader of site environment can be met, once and false drop rate reached and wanted
It asks, there is no need to carry out car plate classifier training again, iteration can be stopped.
In addition, in the embodiment of the present application, live no car scene is detected according to the original car plate detection classifier described,
After the static negative sample for collecting scene, can also include：
Identify simple static negative sample in the static negative sample and difficult static negative sample, and by the simple static
Negative sample is added in the original training set of the car license recognition equipment；
Whether the image-region where judging the difficult static negative sample meets shielding requirements；If it is satisfied, shielding institute
State the image-region where difficult static negative sample；
If conditions are not met, training third car plate grader, the third car plate classification according to the difficult static negative sample
Device is for judging whether the car plate that the second car plate grader identifies is effective car plate.
Alternatively, if conditions are not met, judging the image-region where the difficult static negative sample within the preset period
Whether move, if do not moved, the difficult static negative sample is added to the institute of the car license recognition equipment
It states in original training set.
The engineering that the static background at scene can be trained to together with original negative sample is selected at random.For in field scene
Simple background negative sample, as soon as a Weak Classifier can be easily found filtering out, or filtered in preceding several layers of strong classifier
Fall.Complicated or difficult background negative sample can be retained always, and it is increasing to obtain weight, i.e., is increasingly closed
Note, it is more likely that can be filtered in more subsequent layer.
Scene is detected in the case of no vehicle using new car plate grader after training.Have some negative samples
This very close car plate, these negative samples can not be removed to layer after very, must just be removed using method for distinguishing.Example
Such as：
If 1. difficult negative sample be not the image occurred center or vehicle without going past can be difficult
The regions shield that negative sample occurs, i.e. car plate grader are not detected the region.
2. if the region that difficult negative sample occurs cannot shield, one point can be trained specifically for the negative sample
Class device judges whether it is not car plate once car plate detection of classifier a to car plate using the detection of classifier.
3. can according to the characteristic of car plate, such as mobility, if detect car plate is detected from for the first time, and
It does not move within a certain period of time, it is effective car plate to be considered as this car plate not.Such as fence is possible to not effectively remove, it may
Recognize result is " 1 " entirely, " Y ", " T ", characters such as " L ", then coordinate displacement information, it is also assumed that car plate is invalid.
Can ensure that so new car plate grader hardly at the scene without vehicle in the case of background detection at vehicle
Board carries out car plate detection using new car plate grader.
For the static sample of difficulty that scene cannot remove, the present invention is proposed by adding shielding area, special point of training
The methods of class device and combination displacement information are removed.For dynamic flase drop due to unpredictable, removal is relatively difficult, but if
Static flase drop can be effectively removed, and reduces dynamic flase drop as far as possible, lower false drop rate can be reached.
Above-described embodiment is embodiment of the method provided by the present application, and corresponding above method embodiment, the application also provides one
Kind car license recognition equipment.
Fig. 4 is a kind of structural schematic diagram of car license recognition equipment provided by the present application.
It please refers to shown in Fig. 4, car license recognition equipment provided by the embodiments of the present application, car plate knowledge is carried out at the scene for arranging
Not, the car license recognition equipment includes the original car plate detection classifier trained according to original training set, the car license recognition equipment
Further include：
Static negative sample collection module 1 is collected for the no car scene according to the original car plate detection classifier detection scene
The static negative sample at scene；The static negative sample be the scene no car scene in by the original car plate detection classifier flase drop
For the background image of license plate area；
First car plate classifier modules 2, for the static negative sample to be added to described in the car license recognition equipment
In original training set, the first training set is obtained, the first car plate grader is trained according to first training set；
Second car plate classifier modules 3 are collected for the car scene according to the first car plate detection of classifier scene
The dynamic negative sample at scene, the dynamic negative sample is added in first training set, the second training set is obtained, according to institute
It states the second training set and trains the second car plate grader, and Car license recognition is carried out according to the second car plate grader；The dynamic
Negative sample be the scene car scene in by moving image that the first car plate grader flase drop is license plate area.
Corresponding to above method embodiment, licence plate recognition method used by car license recognition equipment provided in this embodiment and
Recognition principle is similar with above method embodiment, and details are not described herein again.
Meanwhile on the basis of the above embodiments, in the embodiment of the present application,
The first car plate classifier modules 2 may include：
Positive sample feature vector units, for using Haar features to each positive sample in first training set into
Row characterization, forms positive sample Haar characteristic vector；
Negative sample feature vector units, for using Haar features to each negative sample in first training set into
Row characterization, forms negative sample Haar characteristic vectors；
Training unit, for utilizing Adaboost algorithm to the positive sample Haar characteristic vector and the negative sample Haar
Feature vector is trained, and obtains the first car plate grader.
The second car plate classifier modules 3 may include：
Detection unit, the video image that the car scene for obtaining car license recognition equipment detection scene obtains, carries
The suspected license plate area in the video image is taken, according in suspected license plate area described in the first car plate detection of classifier
License plate area；
Recognition unit, for being partitioned into multiple characters from the license plate area, according to the vehicle of support vector machines training
Multiple characters are identified in board character recognition model, and judge the recognition confidence of each character；
Dynamic negative sample unit, for judging whether the license plate area has according to the recognition confidence of each character
Effect, if in vain, using the license plate area as the dynamic negative sample at the scene.
The dynamic negative sample unit may include：
First judgment sub-unit, for judging whether the number of the character is 7；
Second judgment sub-unit, for if so, judging whether the recognition confidence of each character is greater than or equal to
First threshold；
Third judgment sub-unit, for if so, judge the recognition confidence of 7 characters and whether be more than or wait
In second threshold；
Subelement is judged, for if it is, judging that the license plate area is effective, otherwise in vain.
The second car plate classifier modules 3 may include：
Statistic unit has parking lot for count the first car plate detection of classifier scene according to preset time interval
The false drop rate of scape, and judge whether the false drop rate is more than third threshold value；
Judging unit, for if so, judging whether the number of the dynamic negative sample being collected into is greater than or equal to the
Four threshold values；
Adding device, for if so, the dynamic negative sample is added in first training set.
The static negative sample collection module 1, including：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and
The simple static negative sample is added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if discontented
Foot trains third car plate grader according to the difficult static negative sample, and the third car plate grader is for judging described the
Whether the car plate that two car plate graders identify is effective car plate.
The static negative sample collection module 1, including：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and
The simple static negative sample is added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if discontented
Foot, whether the image-region where judging the difficult static negative sample within the preset period moves, if do not sent out
The difficult static negative sample is then added in the original training set of the car license recognition equipment by raw movement.
Corresponding to above method embodiment, licence plate recognition method used by car license recognition equipment provided in this embodiment and
Recognition principle is similar with above method embodiment, and details are not described herein again.
By the above technical solution provided by the present application, licence plate recognition method is applied to the car license recognition equipment of arrangement at the scene
In, the car license recognition equipment includes the original car plate detection classifier trained according to original training set, the licence plate recognition method
According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；The static negative sample
For in the no car scene at the scene by background image that the original car plate detection classifier flase drop is license plate area；By the static state
Negative sample is added in the original training set of the car license recognition equipment, obtains the first training set, is instructed according to described first
Practice collection the first car plate grader of training；According to the car scene at the first car plate detection of classifier scene, the scene of collecting is moved
The dynamic negative sample is added in first training set, obtains the second training set by state negative sample, is instructed according to described second
Practice collection the second car plate grader of training, and Car license recognition is carried out according to the second car plate grader；The dynamic negative sample is
In the car scene at the scene by the first car plate grader flase drop be license plate area moving image.In this way, first collecting
The static negative sample at scene is simultaneously added to original training set, obtains the first training set, and the first car plate is trained using the first training set
Grader removes static negative sample, then reuses the first car plate grader and collects dynamic negative sample and be added to the first training
Collection, obtains the second training set, trains the second car plate grader that can remove the static state being collected into using the second training set and bears sample
Originally the most negative samples being likely to occur with dynamic negative sample, the scene of having been contained in the second training set at this time, these are negative
Sample can be refused by the second car plate grader, accurately identified to car plate to reach, reduce the flase drop of car license recognition equipment
Rate.
It should be noted that each embodiment in this specification is described in a progressive manner, each embodiment weight
Point explanation is all difference from other examples, and the same or similar parts between the embodiments can be referred to each other.
For device class embodiment, since it is basically similar to the method embodiment, so fairly simple, the related place ginseng of description
See the part explanation of embodiment of the method.
Finally, it is to be noted that, herein, relational terms such as first and second and the like be used merely to by
One entity or operation are distinguished with another entity or operation, without necessarily requiring or implying these entities or operation
Between there are any actual relationship or orders.Moreover, the terms "include", "comprise" or its any other variant meaning
Covering non-exclusive inclusion, so that the process, method, article or equipment including a series of elements includes not only that
A little elements, but also include other elements that are not explicitly listed, or further include for this process, method, article or
The intrinsic element of equipment.In the absence of more restrictions, the element limited by sentence "including a ...", is not arranged
Except there is also other identical elements in the process, method, article or apparatus that includes the element.
A kind of licence plate recognition method provided by the present invention and car license recognition equipment are described in detail above, herein
In apply specific case principle and implementation of the present invention are described, the explanation of above example is only intended to sides
Assistant solves the method and its core concept of the present invention；Meanwhile for those of ordinary skill in the art, think of according to the present invention
Think, there will be changes in the specific implementation manner and application range, in conclusion the content of the present specification should not be construed as pair
The limitation of the present invention.
Claims (10)
1. a kind of licence plate recognition method is applied in the car license recognition equipment of arrangement at the scene, is wrapped in the car license recognition equipment
Include the original car plate detection classifier trained according to original training set, which is characterized in that the licence plate recognition method includes：
According to the no car scene at the original car plate detection classifier detection scene, the static negative sample at the scene of collecting；It is described static negative
Sample be the scene no car scene in by background image that the original car plate detection classifier flase drop is license plate area；
The static negative sample is added in the original training set of the car license recognition equipment, the first training set is obtained,
The first car plate grader is trained according to first training set；
According to the car scene at the first car plate detection of classifier scene, the dynamic negative sample at the scene of collecting, by the dynamic
Negative sample is added in first training set, obtains the second training set, and the second car plate point is trained according to second training set
Class device, and Car license recognition is carried out according to the second car plate grader；The dynamic negative sample is the car scene at the scene
It is middle by the first car plate grader flase drop be license plate area moving image；
It is described that first car plate grader is trained according to first training set, including：
Each positive sample in first training set is characterized using Haar features, formed positive sample Haar features to
Amount；
Each negative sample in first training set is characterized using Haar features, formed negative sample Haar features to
Amount；
The positive sample Haar characteristic vector and the negative sample Haar characteristic vectors are trained using Adaboost algorithm,
Obtain the first car plate grader；
The car scene according to the first car plate detection of classifier scene, the dynamic negative sample at the scene of collecting, including：
The video image that the car scene at car license recognition equipment detection scene obtains is obtained, is extracted in the video image
Suspected license plate area, according to the license plate area in suspected license plate area described in the first car plate detection of classifier；
Multiple characters are partitioned into from the license plate area, the Recognition of License Plate Characters model pair trained according to support vector machines
Multiple characters are identified, and judge the recognition confidence of each character；
Judge whether the license plate area is effective according to the recognition confidence of each character, if in vain, by the car plate
Dynamic negative sample of the region as the scene.
2. licence plate recognition method according to claim 1, which is characterized in that the identification of each character of the basis is set
Reliability judges whether the license plate area is effective, including：
Judge whether the number of the character is 7；
If so, judging whether the recognition confidence of each character is greater than or equal to first threshold；
If so, judge the recognition confidence of 7 characters and whether be greater than or equal to second threshold；
If it is, judging that the license plate area is effective, otherwise in vain.
3. licence plate recognition method according to claim 1, which is characterized in that described that the dynamic negative sample is added to institute
It states in the first training set, including：
The false drop rate of the car scene at the first car plate detection of classifier scene is counted according to preset time interval, and is judged
Whether the false drop rate is more than third threshold value；
If so, judging whether the number for the dynamic negative sample being collected into is greater than or equal to the 4th threshold value；
If so, the dynamic negative sample is added in first training set.
4. licence plate recognition method according to claim 1, which is characterized in that described according to the original car plate detection classifier
Live no car scene is detected, after the static negative sample at the scene of collecting, further includes：
It identifies the simple static negative sample in the static negative sample and difficult static negative sample, and the simple static is born into sample
Originally it is added in the original training set of the car license recognition equipment；
Whether the image-region where judging the difficult static negative sample meets shielding requirements；
If it is satisfied, the image-region where the shielding difficult static negative sample, if conditions are not met, according to described difficult static
Negative sample trains third car plate grader, and the third car plate grader is for judging what the second car plate grader identified
Whether car plate is effective car plate.
5. licence plate recognition method according to claim 1, which is characterized in that described according to the original car plate detection classifier
Live no car scene is detected, after the static negative sample at the scene of collecting, further includes：
It identifies the simple static negative sample in the static negative sample and difficult static negative sample, and the simple static is born into sample
Originally it is added in the original training set of the car license recognition equipment；
Whether the image-region where judging the difficult static negative sample meets shielding requirements；
If it is satisfied, the image-region where the shielding difficult static negative sample, if conditions are not met, within the preset period
Whether the image-region where judging the difficult static negative sample moves, if do not moved, by the difficulty
Static negative sample is added in the original training set of the car license recognition equipment.
6. a kind of car license recognition equipment carries out Car license recognition at the scene for arranging, the car license recognition equipment includes basis
The original car plate detection classifier of original training set training, which is characterized in that the car license recognition equipment further includes：
Static negative sample collection module collects scene for the no car scene according to the original car plate detection classifier detection scene
Static negative sample；The static negative sample be the scene no car scene in by the original car plate detection classifier flase drop be vehicle
The background image in board region；
First car plate classifier modules, the original instruction for the static negative sample to be added to the car license recognition equipment
Practice and concentrate, obtain the first training set, the first car plate grader is trained according to first training set；
Second car plate classifier modules collect scene for the car scene according to the first car plate detection of classifier scene
Dynamic negative sample, the dynamic negative sample is added in first training set, the second training set is obtained, according to described
Two training sets train the second car plate grader, and carry out Car license recognition according to the second car plate grader；The dynamic bears sample
This be the scene car scene in by moving image that the first car plate grader flase drop is license plate area；
The first car plate classifier modules, including：
Positive sample feature vector units, for carrying out table to each positive sample in first training set using Haar features
Sign forms positive sample Haar characteristic vector；
Negative sample feature vector units, for carrying out table to each negative sample in first training set using Haar features
Sign forms negative sample Haar characteristic vectors；
Training unit, for utilizing Adaboost algorithm to the positive sample Haar characteristic vector and the negative sample Haar features
Vector is trained, and obtains the first car plate grader；
The second car plate classifier modules, including：
Detection unit, the video image that the car scene for obtaining car license recognition equipment detection scene obtains, extracts institute
The suspected license plate area in video image is stated, according to the car plate in suspected license plate area described in the first car plate detection of classifier
Region；
Recognition unit, for being partitioned into multiple characters from the license plate area, according to the car plate word of support vector machines training
Multiple characters are identified in symbol identification model, and judge the recognition confidence of each character；
Dynamic negative sample unit, for judging whether the license plate area is effective according to the recognition confidence of each character,
If invalid, using the license plate area as the dynamic negative sample at the scene.
7. car license recognition equipment according to claim 6, which is characterized in that the dynamic negative sample unit, including：
First judgment sub-unit, for judging whether the number of the character is 7；
Second judgment sub-unit is used for if so, judging whether the recognition confidence of each character is greater than or equal to first
Threshold value；
Third judgment sub-unit, be used for if so, judge the recognition confidence of 7 characters and whether more than or equal to the
Two threshold values；
Subelement is judged, for if it is, judging that the license plate area is effective, otherwise in vain.
8. car license recognition equipment according to claim 6, which is characterized in that the second car plate classifier modules, including：
Statistic unit, the car scene for counting according to preset time interval the first car plate detection of classifier scene
False drop rate, and judge whether the false drop rate is more than third threshold value；
Judging unit, for if so, judging whether the number for the dynamic negative sample being collected into is greater than or equal to the 4th threshold
Value；
Adding device, for if so, the dynamic negative sample is added in first training set.
9. car license recognition equipment according to claim 6, which is characterized in that the static negative sample collection module, including：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and by institute
Simple static negative sample is stated to be added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if conditions are not met, root
Third car plate grader is trained according to the difficult static negative sample, the third car plate grader is for judging second car plate
Whether the car plate that grader identifies is effective car plate.
10. car license recognition equipment according to claim 6, which is characterized in that the static negative sample collection module, packet
It includes：
Recognition unit, the simple static negative sample in the static negative sample and difficult static negative sample for identification, and by institute
Simple static negative sample is stated to be added in the original training set of the car license recognition equipment；
Whether judging unit meets shielding requirements for the image-region where judging the difficult static negative sample；
Screen unit is used for the image-region if it is satisfied, where the shielding difficult static negative sample, if conditions are not met,
Whether the image-region where judging the difficult static negative sample in the preset period moves, if do not moved
It is dynamic, then the difficult static negative sample is added in the original training set of the car license recognition equipment.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410742690.8A CN104361366B (en) | 2014-12-08 | 2014-12-08 | A kind of licence plate recognition method and car license recognition equipment |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201410742690.8A CN104361366B (en) | 2014-12-08 | 2014-12-08 | A kind of licence plate recognition method and car license recognition equipment |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN104361366A CN104361366A (en) | 2015-02-18 |
| CN104361366B true CN104361366B (en) | 2018-10-30 |
Family
ID=52528624
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201410742690.8A Expired - Fee Related CN104361366B (en) | 2014-12-08 | 2014-12-08 | A kind of licence plate recognition method and car license recognition equipment |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN104361366B (en) |
Families Citing this family (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106295635A (en) * | 2016-02-17 | 2017-01-04 | 北京智芯原动科技有限公司 | Licence plate recognition method and system |
| CN107016356A (en) * | 2017-03-21 | 2017-08-04 | 乐蜜科技有限公司 | Certain content recognition methods, device and electronic equipment |
| CN112149707B (en) * | 2019-06-28 | 2024-06-14 | 商汤集团有限公司 | Image acquisition control method, device, medium and equipment |
| CN111310850B (en) * | 2020-03-02 | 2023-06-16 | 杭州雄迈集成电路技术股份有限公司 | License plate detection model construction method and system, license plate detection method and system |
| CN113496227A (en) * | 2020-04-08 | 2021-10-12 | 顺丰科技有限公司 | Training method and device of character recognition model, server and storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101520841A (en) * | 2009-03-10 | 2009-09-02 | 北京航空航天大学 | Real-time and anti-interference method for positioning license plate in high-definition TV video |
| CN103390156A (en) * | 2012-11-05 | 2013-11-13 | 深圳市捷顺科技实业股份有限公司 | License plate recognition method and device |
| KR20140093407A (en) * | 2013-01-18 | 2014-07-28 | 광주과학기술원 | Recognition device, vehicle model recognition apparatus and method |
| CN104408475A (en) * | 2014-12-08 | 2015-03-11 | 深圳市捷顺科技实业股份有限公司 | Vehicle license plate identification method and vehicle license plate identification equipment |
-
2014
- 2014-12-08 CN CN201410742690.8A patent/CN104361366B/en not_active Expired - Fee Related
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101520841A (en) * | 2009-03-10 | 2009-09-02 | 北京航空航天大学 | Real-time and anti-interference method for positioning license plate in high-definition TV video |
| CN103390156A (en) * | 2012-11-05 | 2013-11-13 | 深圳市捷顺科技实业股份有限公司 | License plate recognition method and device |
| KR20140093407A (en) * | 2013-01-18 | 2014-07-28 | 광주과학기술원 | Recognition device, vehicle model recognition apparatus and method |
| CN104408475A (en) * | 2014-12-08 | 2015-03-11 | 深圳市捷顺科技实业股份有限公司 | Vehicle license plate identification method and vehicle license plate identification equipment |
Non-Patent Citations (1)
| Title |
|---|
| 基于Adaboost算法的驾驶员眨眼识别;赵雪竹等;《电子产品世界》;20090904;第31-32、42页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN104361366A (en) | 2015-02-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104408475B (en) | A kind of licence plate recognition method and car license recognition equipment | |
| CN104361366B (en) | A kind of licence plate recognition method and car license recognition equipment | |
| Saha et al. | License Plate localization from vehicle images: An edge based multi-stage approach | |
| Ren et al. | Detecting and positioning of traffic incidents via video‐based analysis of traffic states in a road segment | |
| Guo et al. | Nighttime vehicle lamp detection and tracking with adaptive mask training | |
| CN109977897A (en) | A kind of ship's particulars based on deep learning recognition methods, application method and system again | |
| Piciarelli et al. | Surveillance-oriented event detection in video streams | |
| CN106991820A (en) | Violation vehicle processing method and processing device | |
| Velastin et al. | People detection and pose classification inside a moving train using computer vision | |
| CN112270253A (en) | High-altitude parabolic detection method and device | |
| Malhi et al. | Vision based intelligent traffic management system | |
| CN105046948A (en) | System and method of monitoring illegal traffic parking in yellow grid line area | |
| Ketcham et al. | Recognizing the Illegal Parking Patterns of Cars on the Road in Front of the Bus Stop Using the Support Vector Machine | |
| Lin et al. | Improved traffic sign recognition for in-car cameras | |
| Tao et al. | A three-stage framework for smoky vehicle detection in traffic surveillance videos | |
| Wang et al. | Traffic camera anomaly detection | |
| Ali et al. | License plate recognition system | |
| CN106384089A (en) | Human body reliable detection method based on lifelong learning | |
| CN204884166U (en) | Regional violating regulations parking monitoring devices is stopped to traffic taboo | |
| Saluky et al. | Abandoned Object Detection Method Using Convolutional Neural Network | |
| CN110210351A (en) | A kind of vehicle characteristics extracting method based on deep neural network | |
| Parsola et al. | Automated system for road extraction and traffic volume estimation for traffic jam detection | |
| Ambata et al. | Public and private vehicle quantification and classification using vehicle detection and recognition | |
| CN107993446A (en) | A kind of traffic prohibition parking area domain parking offense monitoring device | |
| Oh et al. | Development of an integrated system based vehicle tracking algorithm with shadow removal and occlusion handling methods |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CF01 | Termination of patent right due to non-payment of annual fee |
Granted publication date: 20181030 |
|
| CF01 | Termination of patent right due to non-payment of annual fee |