CN111783421A - Character similarity calculation method for fusion of radio frequency identification and license plate identification data - Google Patents

Character similarity calculation method for fusion of radio frequency identification and license plate identification data Download PDF

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CN111783421A
CN111783421A CN202010574967.6A CN202010574967A CN111783421A CN 111783421 A CN111783421 A CN 111783421A CN 202010574967 A CN202010574967 A CN 202010574967A CN 111783421 A CN111783421 A CN 111783421A
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character
matrix
characters
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蒋遂平
傅晗
叶青
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Beijing Institute of Computer Technology and Applications
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Abstract

The invention relates to a character similarity calculation method for fusing radio frequency identification and license plate identification data, which comprises the following steps: character recognition error counting: accumulating the number plate recognition to wrongly recognize one character in the number plate as the number of other characters; character recognition error statistics step: calculating statistics such as mean value, variance and the like according to the character error count of license plate recognition; and a character similarity calculation step: and calculating the similarity of the characters according to the recognition errors and the same dosage of the characters of the license plate recognition. The character similarity calculated by the method can accord with the actual situation, the result of the fusion of the radio frequency identification and the license plate identification data is fully explained, a basis is provided for improving the license plate identification, and the method plays an important role in the application of automobile electronic identification in the field of intelligent transportation.

Description

Character similarity calculation method for fusion of radio frequency identification and license plate identification data
Technical Field
The invention relates to a character similarity calculation method, in particular to a character similarity calculation method for fusing radio frequency identification and license plate identification data.
Background
Radio Frequency Identification (RFID) technology has the advantages of fast Identification, long-distance Identification, multi-target Identification, non-line-of-sight Identification and the like, and is widely applied to automobile electronic Identification in the field of intelligent transportation. The radio frequency identification tag storing the motor vehicle number plate is installed on the motor vehicle, the radio frequency identification reader-writer is installed above the road, and when the motor vehicle passes through a working area for reading and writing by radio frequency identification, the motor vehicle number plate can be accurately identified.
A License plate recognition (VLPR) technology for recognizing a number plate of a motor Vehicle by using an image or video mode is also widely applied to the field of intelligent transportation. By installing the camera above the road and installing the computer license plate recognition software, when the motor vehicle passes through the working range of the camera, the image or video of the motor vehicle is collected by the camera, the license plate recognition software can recognize the license plate of the motor vehicle and can keep the image or video as evidence.
Because the two technologies have advantages and disadvantages, the two technologies need to be fused in practical application so as to fully utilize the high accuracy of radio frequency identification and the evidence capability of license plate identification. In order to realize the fusion of the two, the similarity of the license plates of the two recognition results, especially the similarity of characters, needs to be calculated.
201410291328.3 method and system for integrating radio frequency identification and license plate identification discloses a method for calculating character similarity, which adopts the similarity of character images as the similarity between characters, the similarity is symmetrical, and for two characters Ci and Cj, the probability of identifying Ci as Cj and the probability of identifying Cj as Ci are the same.
However, in practical applications, it is found that the probability of identifying Ci as Cj is different from the probability of identifying Cj as Ci, and it is difficult for a user to understand the result of data fusion.
Therefore, a new method for calculating the similarity of characters is urgently needed, the calculated similarity of characters can accord with the actual situation, and the result of fusing radio frequency identification data and license plate identification data is fully explained.
Disclosure of Invention
The invention aims to provide a character similarity calculation method for fusing radio frequency identification and license plate identification data, which is used for solving the problems in the prior art.
The invention relates to a character similarity calculation method for fusing radio frequency identification and license plate identification data, which comprises the following steps of; performing a character recognition error count, comprising: establishing two counting matrixes according to the fact that the motor vehicle license plate contains two types of characters, namely numeric alphabetical characters and Chinese characters; two counting matrixes, wherein the row of each matrix is a character C obtained by radio frequency identificationiThe columns of the matrix are characters C obtained by license plate recognitionjInitially, all elements d of the two counting matricesijAre both 0; pairing the motor vehicle license plates obtained by radio frequency identification and the motor vehicle license plates obtained by license plate identification, respectively counting the number letter matrix and the Chinese character matrix, and if the characters C in the motor vehicle license plates obtained by radio frequency identification are countediIs erroneously recognized as character C in the motor vehicle license plate obtained by license plate recognitionjThen the element d in the ith row and the jth column of the matrixijIncrement by 1 if the character C in the motor vehicle license plate is obtained by radio frequency identificationiCorrectly identified as character C in motor vehicle license plate obtained by license plate identificationiThen the element d in the ith row and ith column of the matrixiiThe change is not changed; calculating statistics of two counting matrixes according to the character error count of the license plate recognition; and calculating the similarity of the characters of the two counting matrixes according to the recognition error and the same dosage of the characters of the license plate recognition.
According to an embodiment of the method for calculating the similarity of characters for fusing the radio frequency identification data and the license plate identification data, the first matrix of the two counting matrices is a 34-row 34-column number letter matrix, and the number letters are: 0. 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, Q, R, S, T, U, V, W, X, Y, and Z; the second matrix is a matrix of 31 rows and 31 columns of Chinese characters: jing, jin, Ji, jin, Mongolia, Liao, Ji, Hei, Shanghai, Su, Zhe, Hui, Min, Jiang, Lu, Yu, Hui, Xiang, Yue, Gui, Qiong, Yu, Chuan, Gui, Yun, Ting, Zangshan, Gan, Ning and Qing.
According to one embodiment of the character similarity calculation method for fusing the radio frequency identification data and the license plate identification data, statistics comprise a mean value and a variance.
According to an embodiment of the character similarity calculation method for fusing the radio frequency identification data and the license plate identification data, the calculation of the mean value and the variance comprises the following steps:
respectively calculating a digital letter matrix and a Chinese character matrix, and calculating the mean value mu and the variance sigma of all off-diagonal elements in the matrix;
μ＝∑i,j(i≠j)dij/(N2-N)；
Figure BDA0002550650300000031
in the formula for calculating the mean μ and variance σ, N is the number of rows in the matrix.
According to an embodiment of the method for calculating the similarity of characters for fusing radio frequency identification data and license plate identification data, the calculating the similarity of characters of two counting matrixes comprises the following steps:
if the character C in the motor vehicle license plate is obtained by radio frequency identificationiIs repeatedly and wrongly recognized as character C in the motor vehicle license plate obtained by license plate recognitionjThen C isiAnd CjHas a large similarity of sijThe calculation is as follows:
sij＝(dij-μ+3×σ)/(6×σ)
after the calculation, correction is performed. If s isij<0, then sij0; if s isij>1, then sij＝1；sii＝1。
The character similarity calculation method for fusing the radio frequency identification data and the license plate identification data can be used for calculating the character similarity according with the actual situation, fully explaining the result of fusing the radio frequency identification data and the license plate identification data and providing a basis for improving the license plate identification.
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Fig. 1 is a schematic flow chart of a character similarity calculation method for fusing radio frequency identification and license plate identification data according to the present invention.
Detailed Description
In order to make the objects, contents, and advantages of the present invention clearer, the following detailed description of the embodiments of the present invention will be made in conjunction with the accompanying drawings and examples.
The invention provides a character similarity calculation method for fusing radio frequency identification and license plate identification data, and fig. 1 is a schematic flow chart of the method, as shown in fig. 1, the method comprises the following steps:
(1) character recognition error counting: accumulating the number plate recognition to wrongly recognize one character in the number plate as the number of other characters;
when the motor vehicle license plate is implemented specifically, the motor vehicle license plate comprises two types of characters, namely numeric alphabetical characters, Chinese characters and the like, and the two types of characters have specific positions in the license plate, so that the two types of characters cannot be mutually mistakenly recognized by license plate recognition. Thus. Two count matrices may be established.
The first matrix is a 34 row 34 column alphanumeric matrix, the alphanumeric characters being: 0. 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, Q, R, S, T, U, V, W, X, Y, Z. The second matrix is a matrix of 31 rows and 31 columns of Chinese characters: jing, jin, Ji, jin, Mongolia, Liao, Ji, Hei, Shanghai, Su, Zhe, Hui, Min, Jiang, Lu, Yu, Hui, Xiang, Yue, Gui, Qiong, Yu, Chuan, Gui, Yun, Ting, Shaan, Gan, Ning and Qing.
In the two matrices, the rows of the matrix are the characters (actual characters) Ci obtained by radio frequency identification, and the columns of the matrix are the characters Cj (identification characters) obtained by license plate identification. Initially, all elements dij of both matrices are 0.
And automatically or manually pairing the motor vehicle license plates obtained by radio frequency identification and the motor vehicle license plates obtained by license plate identification. And counting the number letter matrix and the Chinese character matrix respectively. If the character Ci in the motor vehicle license plate obtained by radio frequency identification is wrongly identified as the character Cj in the motor vehicle license plate obtained by license plate identification, the element dij of the ith row and the jth column in the matrix is increased by 1. If the character Ci in the motor vehicle license plate obtained by radio frequency identification is correctly identified as the character Ci in the motor vehicle license plate obtained by license plate identification, the element dii in the ith row and the ith column in the matrix is unchanged.
The resulting two matrices are generally not symmetrical, e.g., the character "T" is easily and incorrectly recognized as character "1" by license plate recognition, but the character "1" is not easily and incorrectly recognized as character "T" by license plate recognition, so that d1T and dT1 are different.
(2) Character recognition error statistics step: calculating statistics such as mean value, variance and the like according to the character error count of license plate recognition;
in specific implementation, the numeric letter matrix and the Chinese character matrix are respectively calculated, and the mean value mu and the variance sigma of all off-diagonal elements in the matrix are calculated.
μ＝∑i,j(i≠j)dij/(N2-N)
Figure BDA0002550650300000051
In the formula for calculating the mean μ and variance σ, N is the number of rows in the matrix.
(3) And a character similarity calculation step: and calculating the similarity of the characters according to the recognition errors and the same dosage of the characters of the license plate recognition.
In the specific implementation, if the character C in the motor vehicle license plate obtained by the radio frequency identification isiIs repeatedly and wrongly recognized as character C in the motor vehicle license plate obtained by license plate recognitionjThen C isiAnd CjHas a large similarity of sijThe calculation is as follows:
sij＝(dij-μ+3×σ)/(6×σ)
after the calculation, correction is performed. If s isij<0, then sij0; if s isij>1, then sij＝1；sii＝1。
Compared with the existing method for calculating the character similarity, the method has the advantages that the calculated character similarity can accord with the actual situation, the result of fusion of radio frequency identification and license plate identification data is fully explained, and a basis is provided for improving license plate identification. The invention plays an important role in the application of automobile electronic identification in the field of intelligent transportation.
There are, of course, many other embodiments of the invention and it will be apparent to those skilled in the art that various changes and modifications can be made herein without departing from the spirit and scope of the invention, e.g., by varying the coefficients of variance, and such corresponding changes and modifications are intended to be covered by the appended claims.

Claims (6)

1. A character similarity calculation method for fusing radio frequency identification and license plate identification data is characterized by comprising the following steps of;
performing a character recognition error count, comprising:
establishing two counting matrixes according to the fact that the motor vehicle license plate contains two types of characters, namely numeric alphabetical characters and Chinese characters;
two counting matrixes, wherein the row of each matrix is a character C obtained by radio frequency identificationiThe columns of the matrix are characters C obtained by license plate recognitionjInitially, all elements d of the two counting matricesijAre both 0;
pairing the motor vehicle license plates obtained by radio frequency identification and the motor vehicle license plates obtained by license plate identification, respectively counting the number letter matrix and the Chinese character matrix, and if the characters C in the motor vehicle license plates obtained by radio frequency identification are countediIs erroneously recognized as character C in the motor vehicle license plate obtained by license plate recognitionjThen the element d in the ith row and the jth column of the matrixijIncrement by 1 if the character C in the motor vehicle license plate is obtained by radio frequency identificationiCorrectly identified as character C in motor vehicle license plate obtained by license plate identificationiThen the element d in the ith row and ith column of the matrixiiThe change is not changed;
calculating statistics of two counting matrixes according to the character error count of the license plate recognition;
and calculating the similarity of the characters of the two counting matrixes according to the recognition error and the same dosage of the characters of the license plate recognition.
2. The method for calculating character similarity for use in fusing radio frequency identification and license plate identification data of claim 1, wherein, in two count matrices,
the first matrix is a 34 row 34 column alphanumeric matrix, the alphanumeric characters being: 0. 1, 2, 3, 4, 5, 6, 7, 8, 9, A, B, C, D, E, F, G, H, J, K, L, M, N, P, Q, R, S, T, U, V, W, X, Y, and Z;
the second matrix is a matrix of 31 rows and 31 columns of Chinese characters: jing, jin, Ji, jin, Mongolia, Liao, Ji, Hei, Shanghai, Su, Zhe, Hui, Min, Jiang, Lu, Yu, Hui, Xiang, Yue, Gui, Qiong, Yu, Chuan, Gui, Yun, Ting, Zangshan, Gan, Ning and Qing.
3. The method of claim 1, wherein the statistics include mean and variance.
4. The method of claim 2, wherein computing the mean and variance comprises:
respectively calculating a digital letter matrix and a Chinese character matrix, and calculating the mean value mu and the variance sigma of all off-diagonal elements in the matrix;
μ＝∑i,j(i≠j)dij/(N2-N)；
Figure FDA0002550650290000021
in the formula for calculating the mean μ and variance σ, N is the number of rows in the matrix.
5. The method of claim 4, wherein calculating the similarity of characters of two counting matrices comprises:
if the character C in the motor vehicle license plate is obtained by radio frequency identificationiIs repeatedly and wrongly recognized as character C in the motor vehicle license plate obtained by license plate recognitionjThen C isiAnd CjHas a large similarity of sijThe calculation is as follows:
sij＝(dij-μ+3×σ)/(6×σ)
after the calculation, correction is performed. If s isij<0, then sij0; if s isij>1, then sij＝1；sii＝1。
6. The method of claim 1, wherein the establishing two counting matrices comprises:
a matrix with 34 rows and 34 columns and a matrix with 31 rows and 31 columns are established, which respectively correspond to the number letters and the provincial administrative division abbreviation in the motor vehicle license plate.
CN202010574967.6A 2020-06-22 2020-06-22 Character similarity calculation method for fusion of radio frequency identification and license plate identification data Pending CN111783421A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113077018A (en) * 2021-06-07 2021-07-06 浙江大华技术股份有限公司 Target object identification method and device, storage medium and electronic device
CN115100610A (en) * 2021-10-18 2022-09-23 公安部交通管理科学研究所 Method for identifying digital identity information of electric bicycle

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070260966A1 (en) * 2003-11-28 2007-11-08 Ki-Hyun Kim Error Correction Method and Apparatus for Low Density Parity Check
CN104077570A (en) * 2014-06-25 2014-10-01 北京计算机技术及应用研究所 Method and system for fusing radio frequency identification and vehicle license plate recognition
CN105224962A (en) * 2014-07-03 2016-01-06 浙江宇视科技有限公司 A kind of similar vehicle license plate extraction method and device

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070260966A1 (en) * 2003-11-28 2007-11-08 Ki-Hyun Kim Error Correction Method and Apparatus for Low Density Parity Check
CN104077570A (en) * 2014-06-25 2014-10-01 北京计算机技术及应用研究所 Method and system for fusing radio frequency identification and vehicle license plate recognition
CN105224962A (en) * 2014-07-03 2016-01-06 浙江宇视科技有限公司 A kind of similar vehicle license plate extraction method and device

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113077018A (en) * 2021-06-07 2021-07-06 浙江大华技术股份有限公司 Target object identification method and device, storage medium and electronic device
CN115100610A (en) * 2021-10-18 2022-09-23 公安部交通管理科学研究所 Method for identifying digital identity information of electric bicycle
CN115100610B (en) * 2021-10-18 2024-05-17 公安部交通管理科学研究所 Identification method for digital identity information of electric bicycle

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