CN107506777A - A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs - Google Patents
A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs Download PDFInfo
- Publication number
- CN107506777A CN107506777A CN201710423827.7A CN201710423827A CN107506777A CN 107506777 A CN107506777 A CN 107506777A CN 201710423827 A CN201710423827 A CN 201710423827A CN 107506777 A CN107506777 A CN 107506777A
- Authority
- CN
- China
- Prior art keywords
- license plate
- image
- character
- hyperplane
- license
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000012706 support-vector machine Methods 0.000 title claims abstract description 39
- 238000000034 method Methods 0.000 title claims abstract description 36
- 230000009466 transformation Effects 0.000 title claims abstract description 15
- 238000012545 processing Methods 0.000 claims abstract description 16
- 230000011218 segmentation Effects 0.000 claims abstract description 6
- 238000012549 training Methods 0.000 claims description 9
- 230000009191 jumping Effects 0.000 claims description 8
- 230000008859 change Effects 0.000 claims description 5
- 238000011426 transformation method Methods 0.000 claims description 5
- 238000000354 decomposition reaction Methods 0.000 claims description 4
- 238000001914 filtration Methods 0.000 claims description 4
- 206010039203 Road traffic accident Diseases 0.000 claims description 3
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 230000008569 process Effects 0.000 claims description 3
- 238000012850 discrimination method Methods 0.000 claims description 2
- 238000006243 chemical reaction Methods 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 7
- 238000005516 engineering process Methods 0.000 description 4
- 230000000694 effects Effects 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 230000006835 compression Effects 0.000 description 1
- 238000007906 compression Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000005259 measurement Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 230000000087 stabilizing effect Effects 0.000 description 1
- 230000001360 synchronised effect Effects 0.000 description 1
- 230000001131 transforming effect Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/24—Classification techniques
- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/22—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition
- G06V10/225—Image preprocessing by selection of a specific region containing or referencing a pattern; Locating or processing of specific regions to guide the detection or recognition based on a marking or identifier characterising the area
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/20—Image preprocessing
- G06V10/30—Noise filtering
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V30/00—Character recognition; Recognising digital ink; Document-oriented image-based pattern recognition
- G06V30/10—Character recognition
- G06V30/14—Image acquisition
- G06V30/148—Segmentation of character regions
- G06V30/153—Segmentation of character regions using recognition of characters or words
-
- 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)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Data Mining & Analysis (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Evolutionary Computation (AREA)
- Evolutionary Biology (AREA)
- General Engineering & Computer Science (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Life Sciences & Earth Sciences (AREA)
- Image Analysis (AREA)
- Traffic Control Systems (AREA)
- Image Processing (AREA)
Abstract
The present invention discloses a kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs, comprises the following steps, step 1, collection vehicle image；Step 2, denoising is carried out to image；Step 3, the position of license plate image is determined according to the edge pixel number in rectangle frame and the size of the ratio between rectangular area；Step 4, the horizontal level of car plate is determined using the method for row scanning to license plate image, the upright position of car plate is determined using the method for small echo layered transformation；Step 5, binary conversion treatment is carried out to each width license plate image, then rank scanning is entered to the license plate image after processing, when single-row edge pixel number is when exceeding threshold range set in advance, then judge that otherwise the row do not include border of the characters on license plate as Character segmentation when forefront includes characters on license plate；Step 6, characters on license plate is identified.The present invention proposes that one kind is accurate, quickly and efficiently solves more Car license recognitions.
Description
Technical Field
The invention relates to the technical field of computer vision image processing, in particular to a real-time multi-license plate identification method and device based on wavelet transformation and a support vector machine.
Background
The license plate recognition technology is an algorithm technology for detecting the license plate of a vehicle in an external actual traffic road scene and accurately recognizing license plate characters of the vehicle. The method comprises the following steps that a common camera is used for obtaining an actual traffic road scene image, and due to the influence of external complex environments such as weather, road conditions and the like, the detection and identification of a license plate by the camera are seriously interfered; moreover, most license plate recognition algorithms can only detect and recognize a single license plate in an image, and cannot accurately position and recognize multiple license plates in the image. At present, the license plate recognition technology has become the research focus of computer graphics, and is widely applied to real life, such as a residential parking timing system, a traffic intersection safety monitoring system and the like.
The license plate detection and the license plate character recognition are two key technologies of the license plate recognition. The license plate detection is the front-end work of license plate recognition, and is particularly important for multi-license plate recognition. Only if the license plate in the image is accurately positioned, the subsequent processing work such as identification and the like can be carried out on the plurality of license plates in the image. The method adopts the existing single-target algorithm to carry out simple expansion to realize the detection and identification of a plurality of license plates, firstly, the detection effect of the method cannot meet the requirement, because a single license plate is clearer, the area of the single license plate in an image is bigger, and a plurality of license plates are distributed in each area of the image, each area is not very big, and the license plates are not particularly clear per se; secondly, the algorithm is simply added to the multi-license plate detection, so that the algorithm complexity is high, and the real-time effect is poor. Therefore, aiming at the existence of illegal events such as vehicle overspeed, illegal overtaking, red light running and the like on the current traffic road, a method capable of realizing multi-license plate recognition is urgently needed to prevent the occurrence of the traffic illegal activities.
Disclosure of Invention
The invention provides a real-time multi-license plate identification method and a real-time multi-license plate identification device based on wavelet transformation and a support vector machine, which can accurately, quickly and effectively solve multi-license plate identification.
The technical scheme of the invention is realized as follows:
a real-time multi-license plate recognition method based on wavelet change and a support vector machine comprises the following steps,
step 1, collecting a vehicle image;
step 2, denoising the image, specifically, performing laplace transform on the image, enhancing the edge of the image, converting the color image into a 256-level gray image, and performing Gaussian blur processing on the image;
step 3, determining the position of the license plate image according to the ratio of the number of edge pixels in the rectangular frame to the rectangular area;
step 4, determining the horizontal position of the license plate by adopting a line scanning method and determining the vertical position of the license plate by adopting a wavelet hierarchical transformation method;
step 5, performing binarization processing on each license plate image, then performing row scanning on the processed license plate image, judging that the current row contains license plate characters when the number of single-row edge pixels exceeds a preset threshold range, and otherwise, judging that the row does not contain the license plate characters and using the license plate characters as character segmentation boundaries;
and 6, recognizing the characters of the license plate, specifically, recognizing the characters of the detected license plate image by using a pre-trained SVM (support vector machine) to obtain license plate information, and recording the position of the license plate in the vehicle image.
The invention also provides a device for identifying multiple license plates in real time by adopting the method, which comprises a video acquisition module, a multi-license plate identification module and a multi-license plate identification module, wherein the video acquisition module is used for acquiring images; the license plate recognition module is used for recognizing the license plate; the power supply module is used for supplying power to the whole system; the storage module is used for storing the system program and the important video; the display module is used for displaying scene shooting videos and video frames with illegal traffic accidents; the video acquisition module, the power module, the storage module and the display module are in signal connection with the card identification module.
The real-time multi-license plate recognition method and device based on wavelet change and the support vector machine can realize accurate detection of a plurality of license plates in the same image and can quickly recognize the license plates, thereby providing great help for monitoring safety of traffic monitoring personnel.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without creative efforts.
FIG. 1 is a flow chart of a real-time multi-license plate recognition method based on wavelet transformation and a support vector machine according to the present invention;
FIG. 2 is a block diagram of a real-time multi-license plate recognition device based on wavelet transformation and a support vector machine;
fig. 3 is a diagram of a wavelet transform decomposition.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a real-time multi-license plate recognition method based on wavelet transformation and support vector machine includes the following steps,
step 1, collecting a vehicle image; the method can acquire the vehicle image of the actual scene through a camera; in the embodiment, the acquisition signal is generated by the processor, the camera is controlled to acquire and decode the measurement image, and the acquisition speed is 35 frames/second.
Step 2, denoising the image, specifically, performing laplace transform on the image, enhancing the edge of the image, converting the color image into a 256-level gray image, and performing Gaussian blur processing on the image;
step 3, determining the position of the license plate image according to the ratio of the number of edge pixels in the rectangular frame to the rectangular area;
step 4, determining the horizontal position of the license plate by adopting a line scanning method and determining the vertical position of the license plate by adopting a wavelet hierarchical transformation method;
step 5, performing binarization processing on each license plate image, then performing row scanning on the processed license plate image, judging that the current row contains license plate characters when the number of single-row edge pixels exceeds a preset threshold range, and otherwise, judging that the row does not contain the license plate characters and using the license plate characters as character segmentation boundaries;
and 6, recognizing the characters of the license plate, specifically, recognizing the characters of the detected license plate image by using a pre-trained SVM (support vector machine) to obtain license plate information, and recording the position of the license plate in the vehicle image.
Further, when step 2 is executed, the specific operation principle is as follows:
step 2-1: according to the Laplace transform template:
step 2-2: laplace edge enhancement processing:wherein (x, y) is the coordinates of the vehicle image in the time domain, and c represents the influence factor of the Laplace operator, and the value range of the influence factor is 0.4-0.7. g (x, y) represents the grayscale value at (x, y) after the enhancement, and f (x, y) is the grayscale value at (x, y) before the enhancement processing.
Step 2-3, changing the color image into a gray image: gray (x, y) ═ 0.3R (x, y) +0.59G (x, y) +0.11B (x, y); wherein R (x, y) is the gray scale value of the R channel, G (x, y) is the gray scale value of the G channel, and B (x, y) is the gray scale value of the B channel;
step 2-4: and (3) carrying out Gaussian filtering denoising treatment, wherein a Gaussian filtering template is as follows:
further, when step 4 is executed, the specific steps include:
step 4-1, scanning lines of a license plate image to determine the horizontal position of the license plate, specifically marking each line from black to white or from white to black as one jump, generating at least two jumps on each character during line scanning, and setting a jump threshold value to be 14 times; scanning is carried out from bottom to top, the jumping number of line scanning is counted, if the jumping point number of a certain line is larger than 14, the line is judged to be the line where the license plate is located, the line is marked as the bottom of the license plate, the line is continuously scanned line by line upwards until the jumping number is smaller than 14, and the line is used as the top of the license plate; in the step, the number plate is considered to have 7 characters, which are influenced by the factors of character fracture, blurring, number plate inclination and the like, so that the threshold value of jumping is set to be 14 times, and if the jumping number of a certain row is more than 14, the row is considered to be the row where the number plate is located, and the row is the bottom of the number plate.
Step 4-2, the vertical projection VPj of each column of the M row image is obtained by:
where f (x, y) represents the gray scale value of the (x, y) point, M is the number of lines in the image, i is the pixel x coordinate, and j is the pixel y coordinate. And according to the gray value of the license plate with the determined horizontal position in the vertical direction, performing three-layer decomposition and reconstruction by adopting a wavelet hierarchical transformation method to determine the vertical position of the license plate. According to the previous experience of the person skilled in the art, the vertical region of the character region of the license plate usually forms a dense peak-valley-peak feature, so that the left and right boundaries of the license plate can be obtained preliminarily. Then according to a wavelet algorithm and an algorithm realized by one-dimensional wavelet transformation, namely a common mallat algorithm, firstly transforming a signal with a larger scale to obtain a low-frequency part and a high-frequency part of the signal, then decomposing the low-frequency part for 2 times, decomposing the low-frequency part into the low-frequency part and the high-frequency part, decomposing for one time as shown in figure 3, extracting useful information from the wavelet to construct the signal (the useful information is the high-frequency part of the wavelet decomposition signal), and further determining the vertical position of the license plate by the method.
Further, when step 6 is executed, the execution step of performing character recognition on the detected license plate image by the pre-trained SVM support vector machine comprises:
step 6-1, training the support vector machine according to the abbreviation of 34 provinces, 26 English letters and 10 numeric characters, wherein the training process is as follows:
collecting training sample data, wherein the sample is a standard character and is a binary figure;
constructing data characteristics of a graphic sample, wherein the data characteristics at least comprise the ratio of the number of character pixels in a rectangle of the character to the number of non-character pixels and the aspect ratio;
constructing a feature vector for the sample according to the data features, and randomly selecting two features from the feature vector to establish a feature space, wherein all the data features relate to;
searching a characteristic hyperplane for distinguishing a sample from a non-sample in a characteristic space according to the sample data, wherein the characteristic hyperplane is used for judging whether the character accords with the data characteristic corresponding to the sample so as to judge whether the character is a corresponding character, and a hyperplane formula in a two-dimensional characteristic space is as follows:
wherein w represents the slope of the hyperplane of the discrimination character, x represents the spatial x coordinate, b represents the intersection line of the feature plane and the Y plane, Y represents the spatial ordinate, and i represents the feature serial number. When the characteristic hyperplane is larger than 1, the characteristic hyperplane belongs to the character class, and when the characteristic hyperplane is smaller than 1, the characteristic hyperplane does not belong to the character class, the coefficients in the optimal hyperplane formula are calculated by a Langerian equation, and the constraint conditions are as follows:
yi[wxi+b]-1≥0,i＝1,2,3,...,l
wherein,the reciprocal of (A) represents the distance between the plane and the sample points in the two types to be separated, and according to the formula, the greater the distance between the hyperplane and the sample points in the two types, the better the distinguishing capability of the hyperplane is,
the lagrangian discriminant is as follows:
according to the Lagrange discrimination method, calculating the following formula to obtain:
obtaining the coefficients w and b of the optimal hyperplane, and finally determining the optimal hyperplane in the space;
and 6-2, when the optimal hyperplane is found in the corresponding pairwise feature spaces, training the SVM (support vector machine).
Further, the characters are identified by using an SVM support vector machine, and the method specifically comprises the following steps:
s1: designing a training sample process of an SVM (support vector machine), generating an xml (extensive markup language) file of a classifier, and recording characteristic values of all characters in the file;
s2: the xml file is loaded with a classifier, and characters are identified with the classifier.
Further, when step 3 is executed, the specific steps include:
step 3-1, searching the outline with rectangular edge in the image;
step 3-2, counting the number of edge pixels falling in each rectangle;
and 3-3, calculating the ratio of the number of edge pixels in the rectangular frame to the area, judging the position of the license plate image when the ratio is larger than a preset threshold value of 0.78, and otherwise, judging the license plate image as a non-license plate area.
Further, when step 5 is executed, the specific steps include:
carrying out binarization processing on each license plate image, wherein the binarization value is 0 or 255, and the threshold value is 80;
and (3) performing column scanning on the image, counting the number of pixels of each column with the pixel value of 255, then calculating the ratio of the number of single-column edge pixels to the length of the column, judging that the current column contains license plate characters when the number of single-column edge pixels exceeds a set threshold range, and otherwise, judging that the column does not contain the license plate characters and using the column as a character segmentation boundary.
A real-time multi-license plate recognition device based on wavelet change and a support vector machine comprises a video acquisition module, a recognition module and a display module, wherein the video acquisition module is used for acquiring images; the license plate recognition module is used for recognizing the license plate; the power supply module is used for supplying power to the whole system; the storage module is used for storing the system program and the important video; the display module is used for displaying scene shooting videos and video frames with illegal traffic accidents; the video acquisition module, the power module, the storage module and the display module are in signal connection with the card identification module.
Go toThe license plate recognition module comprises a Texas instruments (T1 company) TMS320DM8168 digital media processor, wherein the TMS320DM8168 is a DSP processor facing multimedia application and internally integrates 1.2GHzCortexTM-A8 processor and C674x + at 1GHzTMThe floating-point DSP processor and 3 high-definition video coprocessors (HDVICP) are provided, and can support simultaneous processing of 16 paths of 720p30fps synchronous video streams. Due to the high-speed real-time computing capability, the special video interface and the abundant expansion interface, the method is widely applied to the field of multimedia.
The video acquisition module comprises 1 high definition CCD simulation camera and the TVP5158 decoder of TI company, and TVP5158 can automatically control the contrast, reduces the noise, and improves the compression ratio and the whole video quality. The license plate recognition module consists of a TMS320DM8168 digital media processor, and codes are written on the TMS digital media processor to complete the license plate recognition function. The power module adopts a vehicle-mounted power supply to supply power, and 1.8V, 3.3V, 5V and 12V voltages are output through a wide voltage input direct-current level voltage stabilizing chip and a conversion chip to finish power supply for the whole system. The storage module consists of a FLASH memory, a DDR3 internal memory and a hard disk with a SATA interface. The display module is composed of a high-definition display with an HDMI interface, high-definition display of videos is completed, license plates can be clearer, and license plate recognition of the recognition module is facilitated. The device provided by the invention adopts a multithreading compiling mode, realizes parallel operation of acquisition, identification and display, and improves the efficiency of the algorithm.
According to the real-time multi-license plate recognition method and device based on wavelet transformation and the support vector machine, high-efficiency and parallel processors are adopted on hardware, and accurate and effective recognition is carried out on a plurality of license plates in the same image through an algorithm and the support vector machine on an implementation method, so that the real-time multi-license plate recognition method and device can be applied to places such as traffic, parking lots and the like, and great help is provided for supervision personnel.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.
Claims (8)
1. A real-time multi-license plate recognition method based on wavelet change and a support vector machine is characterized by comprising the following steps,
step 1, collecting a vehicle image;
step 2, denoising the image, specifically, performing laplace transform on the image, enhancing the edge of the image, converting the color image into a 256-level gray image, and performing Gaussian blur processing on the image;
step 3, determining the position of the license plate image according to the ratio of the number of edge pixels in the rectangular frame to the rectangular area;
step 4, determining the horizontal position of the license plate by adopting a line scanning method and determining the vertical position of the license plate by adopting a wavelet hierarchical transformation method;
step 5, performing binarization processing on each license plate image, then performing row scanning on the processed license plate image, judging that the current row contains license plate characters when the number of single-row edge pixels exceeds a preset threshold range, and otherwise, judging that the row does not contain the license plate characters and using the license plate characters as character segmentation boundaries;
and 6, recognizing the characters of the license plate, specifically, recognizing the characters of the detected license plate image by using a pre-trained SVM (support vector machine) to obtain license plate information, and recording the position of the license plate in the vehicle image.
2. The real-time multi-license-plate recognition method based on wavelet transformation and support vector machine as claimed in claim 1, wherein when step 2 is executed, the specific operation principle is as follows:
step 2-1: according to the Laplace transform template:
step 2-2: laplace edge enhancement processing:wherein (x, y) is the coordinates of the vehicle image in the time domain, and c represents the influence factor of the Laplace operator, and the value range of the influence factor is 0.4-0.7. g (x, y) represents the grayscale value at (x, y) after the enhancement, and f (x, y) is the grayscale value at (x, y) before the enhancement processing.
Step 2-3, changing the color RGB image into a gray image: gray (x, y) ═ 0.3R (x, y) +0.59G (x, y) +0.11B (x, y); wherein R (x, y) is the gray scale value of the R channel, G (x, y) is the gray scale value of the G channel, and B (x, y) is the gray scale value of the B channel;
step 2-4: and (3) carrying out Gaussian filtering denoising treatment, wherein a Gaussian filtering template is as follows:
3. the real-time multi-license-plate recognition method based on wavelet transformation and support vector machine as claimed in claim 1, wherein when step 4 is executed, the specific steps include:
step 4-1, scanning lines of a license plate image to determine the horizontal position of the license plate, specifically marking each line from black to white or from white to black as one jump, generating at least two jumps on each character during line scanning, and setting a jump threshold value to be 14 times; scanning is carried out from bottom to top, the jumping number of line scanning is counted, if the jumping point number of a certain line is larger than 14, the line is judged to be the line where the license plate is located, the line is marked as the bottom of the license plate, the line is continuously scanned line by line upwards until the jumping number is smaller than 14, and the line is used as the top of the license plate;
step 4-2, the vertical projection VPj of each column of the M row image is obtained by:
where f (x, y) represents the gray scale value of the (x, y) point, M is the number of lines in the image, i is the pixel x coordinate, and j is the pixel y coordinate.
And according to the gray value of the license plate with the determined horizontal position in the vertical direction, performing three-layer decomposition and reconstruction by adopting a wavelet hierarchical transformation method to determine the vertical position of the license plate.
4. The real-time multi-license plate recognition method based on wavelet transformation and support vector machine as claimed in claim 1, wherein when step 6 is executed, the execution step of character recognition of the detected license plate image by the support vector machine of SVM trained in advance comprises:
step 6-1, training the support vector machine according to the abbreviation of 34 provinces, 26 English letters and 10 numeric characters, wherein the training process is as follows:
collecting training sample data, wherein the sample is a standard character and is a binary figure;
constructing data characteristics of a graphic sample, wherein the data characteristics at least comprise the ratio of the number of character pixels in a rectangle of the character to the number of non-character pixels and the aspect ratio;
constructing a feature vector for the sample according to the data features, and randomly selecting two features from the feature vector to establish a feature space, wherein all the data features relate to;
searching a characteristic hyperplane for distinguishing a sample from a non-sample in a characteristic space according to the sample data, wherein the characteristic hyperplane is used for judging whether the character accords with the data characteristic corresponding to the sample so as to judge whether the character is a corresponding character, and a hyperplane formula in a two-dimensional characteristic space is as follows:
wherein w represents the slope of the hyperplane of the discrimination character, x represents the spatial x coordinate, b represents the intersection line of the feature plane and the Y plane, Y represents the spatial ordinate, and i represents the feature serial number. When the characteristic hyperplane is larger than 1, the characteristic hyperplane belongs to the character class, and when the characteristic hyperplane is smaller than 1, the characteristic hyperplane does not belong to the character class, the coefficients in the optimal hyperplane formula are calculated by a Langerian equation, and the constraint conditions are as follows:
yi[wxi+b]-1≥0,i＝1,2,3,...,l
wherein,the inverse of (a) represents the distance of the plane from the sample points of the two classes that need to be separated,
the lagrangian discriminant is as follows:
according to the Lagrange discrimination method, calculating the following formula to obtain:
obtaining the coefficients w and b of the optimal hyperplane, and finally determining the optimal hyperplane in the space;
and 6-2, when the optimal hyperplane is found in the corresponding pairwise feature spaces, training the SVM (support vector machine).
5. The real-time multi-license-plate recognition method based on wavelet transformation and support vector machine as claimed in claim 1, wherein when step 3 is executed, the specific steps include:
step 3-1, searching the outline with rectangular edge in the image;
step 3-2, counting the number of edge pixels falling in each rectangle;
and 3-3, calculating the ratio of the number of edge pixels in the rectangular frame to the area, judging the position of the license plate image when the ratio is larger than a preset threshold value of 0.78, and otherwise, judging the license plate image as a non-license plate area.
6. The real-time multi-license-plate recognition method based on wavelet transformation and support vector machine as claimed in claim 1, wherein when step 5 is executed, the specific steps include:
carrying out binarization processing on each license plate image, wherein the binarization value is 0 or 255, and the threshold value is 80;
and (3) performing column scanning on the image, counting the number of pixels of each column with the pixel value of 255, then calculating the ratio of the number of single-column edge pixels to the length of the column, judging that the current column contains license plate characters when the number of single-column edge pixels exceeds a set threshold range, and otherwise, judging that the column does not contain the license plate characters and using the column as a character segmentation boundary.
7. A device for real-time multi-license plate recognition using the method of claim 1, comprising a video capture module for image capture; the license plate recognition module is used for recognizing the license plate; the power supply module is used for supplying power to the whole system; the storage module is used for storing the system program and the important video; the display module is used for displaying scene shooting videos and video frames with illegal traffic accidents; the video acquisition module, the power module, the storage module and the display module are in signal connection with the card identification module.
8. The apparatus of claim 7, wherein the license plate recognition module comprises a texas instruments TMS320DM8168 digital media processor.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710423827.7A CN107506777A (en) | 2017-06-07 | 2017-06-07 | A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710423827.7A CN107506777A (en) | 2017-06-07 | 2017-06-07 | A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107506777A true CN107506777A (en) | 2017-12-22 |
Family
ID=60678394
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710423827.7A Pending CN107506777A (en) | 2017-06-07 | 2017-06-07 | A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107506777A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108305284A (en) * | 2018-02-28 | 2018-07-20 | 北京奇艺世纪科技有限公司 | A kind of determination method and device of strokes of characters width |
| CN110689000A (en) * | 2018-07-05 | 2020-01-14 | 山东华软金盾软件股份有限公司 | Vehicle license plate identification method based on vehicle license plate sample in complex environment |
| CN111160486A (en) * | 2019-12-31 | 2020-05-15 | 三峡大学 | Fuzzy image classification method based on support vector machine and wavelet decomposition |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009294704A (en) * | 2008-06-02 | 2009-12-17 | Mitsubishi Heavy Ind Ltd | License number recognition device and license number recognition method |
| CN103235938A (en) * | 2013-05-03 | 2013-08-07 | 北京国铁华晨通信信息技术有限公司 | Method and system for detecting and identifying license plate |
| CN107067002A (en) * | 2017-03-09 | 2017-08-18 | 华东师范大学 | Road licence plate recognition method in a kind of dynamic video |
-
2017
- 2017-06-07 CN CN201710423827.7A patent/CN107506777A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP2009294704A (en) * | 2008-06-02 | 2009-12-17 | Mitsubishi Heavy Ind Ltd | License number recognition device and license number recognition method |
| CN103235938A (en) * | 2013-05-03 | 2013-08-07 | 北京国铁华晨通信信息技术有限公司 | Method and system for detecting and identifying license plate |
| CN107067002A (en) * | 2017-03-09 | 2017-08-18 | 华东师范大学 | Road licence plate recognition method in a kind of dynamic video |
Non-Patent Citations (2)
| Title |
|---|
| 刘雄飞等: "基于行扫描和小波变换的车牌定位算法", 《计算机应用与软件》 * |
| 李耀: "复杂环境中的车牌定位算法研究", 《中国优秀硕士学位论文全文数据库工程科技II辑》 * |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108305284A (en) * | 2018-02-28 | 2018-07-20 | 北京奇艺世纪科技有限公司 | A kind of determination method and device of strokes of characters width |
| CN110689000A (en) * | 2018-07-05 | 2020-01-14 | 山东华软金盾软件股份有限公司 | Vehicle license plate identification method based on vehicle license plate sample in complex environment |
| CN110689000B (en) * | 2018-07-05 | 2023-06-23 | 山东华软金盾软件股份有限公司 | Vehicle license plate recognition method based on license plate sample generated in complex environment |
| CN111160486A (en) * | 2019-12-31 | 2020-05-15 | 三峡大学 | Fuzzy image classification method based on support vector machine and wavelet decomposition |
| CN111160486B (en) * | 2019-12-31 | 2023-05-02 | 三峡大学 | Fuzzy image classification method based on support vector machine and wavelet decomposition |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Wei et al. | Multi-vehicle detection algorithm through combining Harr and HOG features | |
| Al-Ghaili et al. | Vertical-edge-based car-license-plate detection method | |
| Babu et al. | Vehicle number plate detection and recognition using bounding box method | |
| WO2019196130A1 (en) | Classifier training method and device for vehicle-mounted thermal imaging pedestrian detection | |
| US8755563B2 (en) | Target detecting method and apparatus | |
| Chandra et al. | Recognition of vehicle number plate using Matlab | |
| CN105095835A (en) | Pedestrian detection method and system | |
| CN107506777A (en) | A kind of real-time more licence plate recognition methods and device based on Wavelet transformation and SVMs | |
| Aung et al. | Automatic license plate detection system for myanmar vehicle license plates | |
| CN105512662A (en) | Detection method and apparatus for unlicensed vehicle | |
| Jaiswal et al. | Survey paper on various techniques of recognition and tracking | |
| Sathiya et al. | Pattern recognition based detection recognition of traffic sign using SVM | |
| CN106203291B (en) | A method of it is detected based on anatomic element analysis and the scene image words of self-adapting dictionary study | |
| CN104834926A (en) | Method and system for character zone extraction | |
| Satish et al. | Edge assisted fast binarization scheme for improved vehicle license plate recognition | |
| Abirami et al. | Accurate vehicle number plate recognition and real-time identification using raspberry pi | |
| Dandu et al. | Vehicular number plate recognition using edge detection and characteristic analysis of national number plates | |
| He et al. | Combining global and local features for detection of license plates in video | |
| Angeline et al. | Tracking and localisation of moving vehicle license plate via Signature Analysis | |
| Zhang et al. | A new text extraction method incorporating local information | |
| Jeffrey et al. | High definition licence plate detection algorithm | |
| Nguyen et al. | Real-time license plate localization based on a new scale and rotation invariant texture descriptor | |
| Karthiprem et al. | Recognizing the moving vehicle while driving on Indian roads | |
| Wang et al. | Research and implementation of license plate location based on histogram division method | |
| Joshi et al. | Automatic Number Plate Recognition and IoT Based Vehicle Tracking |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| CB03 | Change of inventor or designer information | ||
| CB03 | Change of inventor or designer information |
Inventor after: Duan Zhikui Inventor after: Yang Faquan Inventor after: Xiao Yonghao Inventor after: Zhou Yuexia Inventor after: Li Jianhui Inventor after: Chen Jianwen Inventor after: Wang Xingbo Inventor after: Tan Haishu Inventor after: Zhu Zhen Inventor after: Yu Xinmei Inventor after: Wang Dong Inventor after: Fan Yun Inventor before: Duan Zhikui Inventor before: Xiao Yonghao Inventor before: Zhou Yuexia Inventor before: Chen Jianwen Inventor before: Wang Xingbo Inventor before: Tan Haishu Inventor before: Zhu Zhen Inventor before: Yu Xinmei Inventor before: Wang Dong Inventor before: Fan Yun Inventor before: Yang Faquan |
|
| TA01 | Transfer of patent application right | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20210918 Address after: 528000 No. 18, No. 1, Jiangwan, Guangdong, Foshan Applicant after: FOSHAN University Applicant after: FOSHAN POLYTECHNIC Address before: 528000 No. 18, No. 1, Jiangwan, Guangdong, Foshan Applicant before: FOSHAN University |
|
| CB02 | Change of applicant information | ||
| CB02 | Change of applicant information |
Country or region after: China Address after: 528000 No. 18, No. 1, Jiangwan, Guangdong, Foshan Applicant after: Foshan University Applicant after: FOSHAN POLYTECHNIC Address before: 528000 No. 18, No. 1, Jiangwan, Guangdong, Foshan Applicant before: FOSHAN University Country or region before: China Applicant before: FOSHAN POLYTECHNIC |