CN109147340A - Discrimination method, device and the computer equipment of illegal parking case - Google Patents
Discrimination method, device and the computer equipment of illegal parking case Download PDFInfo
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- CN109147340A CN109147340A CN201811004600.XA CN201811004600A CN109147340A CN 109147340 A CN109147340 A CN 109147340A CN 201811004600 A CN201811004600 A CN 201811004600A CN 109147340 A CN109147340 A CN 109147340A
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- illegal parking
- image
- key element
- mentioned
- parking
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Classifications
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- 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
- G06F18/2413—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on distances to training or reference patterns
-
- 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
- G06V10/25—Determination of region of interest [ROI] or a volume of interest [VOI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
- G06V20/54—Surveillance or monitoring of activities, e.g. for recognising suspicious objects of traffic, e.g. cars on the road, trains or boats
-
- 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
Abstract
Present applicant proposes discrimination method, device and the computer equipments of a kind of illegal parking case, wherein the discrimination method of above-mentioned illegal parking case includes: the image for obtaining illegal parking case；Illegal parking key element in described image is detected；Information in the detected illegal parking key element is identified；According to the information in the illegal parking key element of positional relationship and identification acquisition between the detected illegal parking key element, the illegal parking key element, the violation type of the illegal parking case and the compliance of illegal parking case processing are determined.The application may be implemented intelligently to identify the compliance of illegal parking case, reduce the cost of manual examination and verification, and the normalization that can be enforced the law to traffic administration personnel exercises supervision.
Description
[technical field]
This application involves field of computer technology more particularly to a kind of discrimination method, device and the meters of illegal parking case
Calculate machine equipment.
[background technique]
With the fast development of Chinese national economy, the surge of vehicle fleet size results in transport need and increases too fast and draw
A series of problems, such as such as traffic jam of hair, wherein vehicle illegal parking phenomenon be cause obstruction to traffic one it is important because
Element.
Illegal parking behavior for vehicle, judged mostly by traffic administration personnel vehicle whether illegal parking, still
How the compliance of illegal parking case is judged, corresponding solution is not provided in the related technology.
[summary of the invention]
The embodiment of the present application provides discrimination method, device and the computer equipment of a kind of illegal parking case, to realize
Intelligently the compliance of illegal parking case is identified, reduces the cost of manual examination and verification, and can be to traffic administration people
The normalization of member's law enforcement exercises supervision.
In a first aspect, the embodiment of the present application provides a kind of discrimination method of illegal parking case, comprising: obtain illegal stop
The image of vehicle case；Illegal parking key element in described image is detected；To the detected illegal parking
Information in key element is identified；It is crucial according to the detected illegal parking key element, the illegal parking
The information in the illegal parking key element that positional relationship and identification between element obtain, determines the illegal parking case
The compliance of the violation type of part and illegal parking case processing.
Wherein in one possible implementation, the image of the illegal parking case includes: that traffic administration personnel clap
Image, the image at the illegal parking scene for candid photograph of deploying to ensure effective monitoring and control of illegal activities and/or the frame of illegal parking live video at the illegal parking scene taken the photograph
Image.
Wherein in one possible implementation, the illegal parking key element in described image detects
It include: that the size and distribution of color of described image are normalized；Using deep neural network model trained in advance,
Image recognition is carried out to the image after normalized, the area in the image after obtaining the normalized where key element
The classification in domain and the key element, the classification of the key element include following one or combination: the vehicle of vehicle, the vehicle
Board, traffic sign and penalty note.
Wherein in one possible implementation, described using deep neural network model trained in advance, to normalizing
Change before treated image carries out image recognition, further includes: literary using training image and the corresponding mark of the training image
Part is trained training pattern, obtains trained deep neural network model.
Wherein in one possible implementation, described to utilize training image and the corresponding mark text of the training image
Part, training pattern is trained include: will be described in the training image and the training image corresponding mark file input
Training pattern is trained the training pattern using deep neural network algorithm；The corresponding mark text of the training image
Part includes the classification in the region and the key element in the training image where key element；When the training pattern exports
Result mark file corresponding with the training image between error when being less than predetermined threshold, obtain the trained depth
Spend neural network model.
Wherein in one possible implementation, the information in the illegal parking key element include following one or
Combination: the punishment information in the license plate number of the license plate, the type and the penalty note of no parking the marker；It is described right
It includes: the vehicle by license plate recognition technology to the license plate that information in detected illegal parking key element, which carries out identification,
Trade mark code identified, is believed by optical character identification the punishment in the type and the penalty note of no parking the marker
Breath is identified.
Wherein in one possible implementation, it is described according to detected illegal parking key element, it is described disobey
The information in illegal parking key element that positional relationship and identification between method parking key element obtain, determines described illegal
The violation type for case of stopping and the compliance of illegal parking case processing include: the integrality to detected vehicle
It is detected；After determining that the vehicle is complete, the position of detected no parking marker and the vehicle is determined
Relationship；According to the positional relationship of no parking the marker and the vehicle, and the identification marker that obtains that no parking
Type, determine the violation type of the illegal parking case；The violation type of the illegal parking case and identification are obtained
Penalty note in punishment information compare, believed according to the punishment in the violation type of the illegal parking case and the penalty note
Whether breath matches, and determines the compliance of the illegal parking case processing.
Second aspect, the embodiment of the present application provide a kind of identification device of illegal parking case, comprising: obtain module, use
In the image for obtaining illegal parking case；Detection module, the illegal parking in image for obtaining to the acquisition module close
Key element is detected；Identification module, for the information in the detected illegal parking key element of the detection module
It is identified；Determining module, for according to the detected illegal parking key element of the detection module, the illegal parking
The information in illegal parking key element that positional relationship between key element and identification module identification obtain, in determination
State the violation type of illegal parking case and the compliance of above-mentioned illegal parking case processing.
Wherein in one possible implementation, the image of the illegal parking case includes: that traffic administration personnel clap
Image, the image at the illegal parking scene for candid photograph of deploying to ensure effective monitoring and control of illegal activities and/or the frame of illegal parking live video at the illegal parking scene taken the photograph
Image.
Wherein in one possible implementation, the detection module, specifically for the size and face to described image
Color distribution is normalized；Using deep neural network model trained in advance, the image after normalized is carried out
Image recognition, the classification in region and the key element in the image after obtaining the normalized where key element,
The classification of the key element includes following one or combination: vehicle, the license plate of the vehicle, traffic sign and penalty note.
Wherein in one possible implementation, described device further include: training module, for using training image and
The corresponding mark file of the training image, is trained training pattern, obtains trained deep neural network model.
Wherein in one possible implementation, the training module is specifically used for the training image and described
The corresponding mark file of training image inputs the training pattern, is carried out using deep neural network algorithm to the training pattern
Training；The corresponding file that marks of the training image includes region and the key in the training image where key element
The classification of element；When the error between the result of training pattern output mark file corresponding with the training image is less than
When predetermined threshold, the trained deep neural network model is obtained.
Wherein in one possible implementation, the information in the illegal parking key element include following one or
Combination: the punishment information in the license plate number of the license plate, the type and the penalty note of no parking the marker；The knowledge
Other module passes through optical character identification specifically for identifying by license plate number of the license plate recognition technology to the license plate
Punishment information in the type and the penalty note of no parking the marker is identified.
Wherein in one possible implementation, the determining module includes: integrity detection submodule, for institute
The integrality for stating the detected vehicle of detection module is detected；Positional relationship determines submodule, in the integrality
After detection sub-module determines that the vehicle is complete, determine detected no parking the marker of the detection module with it is described
The positional relationship of vehicle；Violation type determination module, for the position according to no parking the marker and the vehicle
Relationship and identification module identification obtain the type of no parking marker, determine disobeying for the illegal parking case
Advise type；Compliance determines submodule, the violation of the illegal parking case for determining the violation type determination module
The punishment information in penalty note that type and identification module identification obtain compares, according to disobeying for the illegal parking case
Whether rule type matches with the punishment information in the penalty note, determines the compliance of the illegal parking case processing.
The third aspect, the embodiment of the present application provide a kind of computer equipment, including memory, processor and are stored in described
It is real when the processor executes the computer program on memory and the computer program that can run on the processor
Now method as described above.
Fourth aspect, the embodiment of the present application provide a kind of non-volatile computer readable storage medium storing program for executing, are stored thereon with meter
Calculation machine program, the computer program realize method as described above when being executed by processor.
It is crucial to the illegal parking in above-mentioned image after the image for obtaining illegal parking case in above technical scheme
Element is detected, and is identified to the information in detected illegal parking key element, then according to detected
The illegal parking that positional relationship and identification between illegal parking key element, above-mentioned illegal parking key element obtain is critical to
Information in element determines the violation type of above-mentioned illegal parking case and the compliance of above-mentioned illegal parking case processing, thus
It may be implemented intelligently to identify the compliance of illegal parking case, reduce the cost of manual examination and verification, and can be to friendship
The normalization of logical law enfrocement official's law enforcement exercises supervision.
[Detailed description of the invention]
Technical solution in ord to more clearly illustrate embodiments of the present application, below will be to needed in the embodiment attached
Figure is briefly described, it should be apparent that, the drawings in the following description are only some examples of the present application, for this field
For those of ordinary skill, without creative efforts, it can also be obtained according to these attached drawings other attached drawings.
Fig. 1 is the flow chart of discrimination method one embodiment of the application illegal parking case；
Fig. 2 is the flow chart of another embodiment of the discrimination method of the application illegal parking case；
Fig. 3 is the flow chart of the discrimination method further embodiment of the application illegal parking case；
Fig. 4 is the flow chart of the discrimination method further embodiment of the application illegal parking case；
Fig. 5 is the structural schematic diagram of identification device one embodiment of the application illegal parking case；
Fig. 6 is the structural schematic diagram of another embodiment of the identification device of the application illegal parking case；
Fig. 7 is the structural schematic diagram of the application computer equipment one embodiment.
[specific embodiment]
In order to better understand the technical solution of the application, the embodiment of the present application is retouched in detail with reference to the accompanying drawing
It states.
It will be appreciated that described embodiments are only a part of embodiments of the present application, instead of all the embodiments.Base
Embodiment in the application, it is obtained by those of ordinary skill in the art without making creative efforts it is all its
Its embodiment, shall fall in the protection scope of this application.
The term used in the embodiment of the present application is only to be not intended to be limiting merely for for the purpose of describing particular embodiments
The application.In the embodiment of the present application and the "an" of singular used in the attached claims, " described " and "the"
It is also intended to including most forms, unless the context clearly indicates other meaning.
Fig. 1 is the flow chart of discrimination method one embodiment of the application illegal parking case, as shown in Figure 1, above-mentioned disobey
Method parking case discrimination method may include:
Step 101, the image of illegal parking case is obtained.
In the present embodiment, when identifying to illegal parking case, the image sources of illegal parking case can there are many,
For example, the image of above-mentioned illegal parking case may include: traffic administration personnel shooting illegal parking scene image,
Deploy to ensure effective monitoring and control of illegal activities candid photograph illegal parking scene image and/or illegal parking live video frame image.
Wherein, above-mentioned illegal parking live video can be the vehicle parking scene of the shooting of the automobile data recorder on vehicle
Video is also possible to the video at the illegal parking scene of onlooker (driver and passenger in pedestrian or Adjacent vehicles) shooting, may be used also
Be traffic administration personnel shooting illegal parking scene video, the present embodiment is not construed as limiting this.
Step 102, the illegal parking key element in above-mentioned image is detected.
Step 103, the information in detected illegal parking key element is identified.
Wherein, above-mentioned illegal parking key element may include following one or combination: vehicle, above-mentioned vehicle license plate,
No parking marker and penalty note；
Information in above-mentioned illegal parking key element may include: the license plate number of above-mentioned license plate, above-mentioned no parking
Punishment information in the type of marker and above-mentioned penalty note.
Wherein, the type of above-mentioned no parking marker may include: parking restrictions, no parking mark, the road Jin Ting
Section and/or pavement etc..
Step 104, according to the position between detected illegal parking key element, above-mentioned illegal parking key element
The information in illegal parking key element that relationship and identification obtain determines the violation type of above-mentioned illegal parking case and above-mentioned
The compliance of illegal parking case processing.
In the discrimination method of above-mentioned illegal parking case, after the image for obtaining illegal parking case, in above-mentioned image
Illegal parking key element detected, the information in detected illegal parking key element is identified, then
According to the positional relationship between detected illegal parking key element, above-mentioned illegal parking key element and identify acquisition
Information in illegal parking key element determines the violation type and the processing of above-mentioned illegal parking case of above-mentioned illegal parking case
Compliance, intelligently the compliance of illegal parking case is identified so as to realize, reduce manual examination and verification at
This, and the normalization that can be enforced the law to traffic administration personnel exercises supervision.
Fig. 2 is the flow chart of another embodiment of the discrimination method of the application illegal parking case, as shown in Fig. 2, this Shen
Please be in embodiment illustrated in fig. 1, step 102 can be with are as follows:
Step 201, the size and distribution of color of above-mentioned image are normalized.
Specifically, the size of above-mentioned image is normalized, is exactly by the size of above-mentioned image according to predetermined big
It is small to be handled, keep the size of above-mentioned image consistent；The distribution of color of above-mentioned image is normalized, be in order to make on
The color for stating image is in same distribution, avoids the contrast of above-mentioned image and/or histogram distribution unbalanced.
Step 202, using deep neural network model trained in advance, image knowledge is carried out to the image after normalized
Not, the classification in region and above-mentioned key element in the image after obtaining above-mentioned normalized where key element, above-mentioned pass
The classification of key element includes following one or combination: vehicle, the license plate of above-mentioned vehicle, traffic sign and penalty note.
Further, before step 202, can also include:
Step 203, using training image and the corresponding mark file of above-mentioned training image, training pattern is trained,
Obtain trained deep neural network model.
Specifically, using training image and the corresponding mark file of above-mentioned training image, being trained to training pattern can
With are as follows: above-mentioned training image and the corresponding mark file of above-mentioned training image are inputted into above-mentioned training pattern, utilize depth nerve
Network algorithm is trained above-mentioned training pattern；Wherein, the corresponding mark file of above-mentioned training image includes above-mentioned training figure
The classification in region and above-mentioned key element as in where key element；When the result and above-mentioned training of the output of above-mentioned training pattern
When error between the corresponding mark file of image is less than predetermined threshold, trained deep neural network model is obtained.
The size of above-mentioned predetermined threshold can voluntarily be set according to system performance and/or realization demand etc. in specific implementation
Fixed, the present embodiment is not construed as limiting the size of above-mentioned predetermined threshold.
Wherein, above-mentioned deep neural network algorithm can use the multi-target detection recognizer based on deep learning, example
Fast area such as based on convolutional neural networks detects (Fast Regions with Convolutional Neural
Network；Hereinafter referred to as: Fast R-CNN), single-lens more boxes detect (Single Shot MultiBox Detector；With
Lower abbreviation: SSD) or only see primary (You Only Look Once；Hereinafter referred to as: YOLO) scheduling algorithm, the present embodiment to this not
It limits.
Fig. 3 is the flow chart of the discrimination method further embodiment of the application illegal parking case, as shown in figure 3, this Shen
Please be in embodiment illustrated in fig. 1, the information in above-mentioned key element includes following one or combination: the license plate number of above-mentioned license plate,
Punishment information in the instruction of above-mentioned traffic sign and above-mentioned penalty note.In this way, step 103 may include:
Step 301, it is identified by license plate number of the license plate recognition technology to above-mentioned license plate, passes through optical character identification
(Optical Character Recognition；Type hereinafter referred to as: OCR) to above-mentioned no parking marker and above-mentioned
Punishment information in penalty note is identified.
Wherein, license plate recognition technology (Vehicle License Plate Recognition；Hereinafter referred to as: VLPR) it is
Video Image identification technology is applied in one of License Plate Identification.License plate recognition technology can be by the licence plate of vehicle
It extracts and identifies from complex background, pass through the skills such as license plate retrieving, image preprocessing, feature extraction, Recognition of License Plate Characters
Art, the information such as identification vehicle identification number, color.
Specifically, it is possible, firstly, to be separated from above-mentioned image to detected license plate region；Then, then
Above-mentioned license plate region is divided into single character, Character segmentation generally uses vertical projection method.Since character is in Vertical Square
The inevitable gap location in intercharacter or character of upward projection obtains near local minimum, and this position should meet
Character writing format, character, size limitation and some other conditions of license plate, therefore Character segmentation is carried out using vertical projection method
There is preferable effect.
Finally, can know based on template matching algorithm and based on artificial neural network algorithm to the character after segmentation
Not.Wherein, character data is scaled by the character binaryzation after segmentation and by its size first based on template matching algorithm
The size of template in library, is then matched with all templates, selects best match as a result.Based on artificial neural network
Algorithm there are two types of: one is first to character carry out feature extraction, neural network distributor is then trained with obtained feature；
Another method is that image is directly inputted network, realizes feature extraction automatically by network until identifying result.
Below to be identified by OCR to the punishment information in the type and above-mentioned penalty note of above-mentioned no parking marker
Process be introduced, following introduction is by taking the type to above-mentioned no parking marker identifies as an example, by OCR to upper
The type for marker of stating that no parking carries out identification can be with are as follows: to parking restrictions, pavement and/or prohibits by OCR and stops section
It is identified.
Specifically, firstly, pre-processing to the image of above-mentioned no parking marker, pretreatment specifically includes that two-value
Change, noise remove and inclination calibration etc..
Wherein, binaryzation is divided to the content of the image of above-mentioned no parking marker, be divided into foreground information with
Background information, can simply define foreground information is black, and background information is white, and here it is binary pictures；
Noise remove is denoised according to the image of the feature of noise to above-mentioned no parking marker；
Inclination is relatively exactly corrected the direction of the image of above-mentioned no parking marker, avoids above-mentioned no parking mark
The image of will object tilts.
Then, printed page analysis carried out to the image of above-mentioned no parking marker, that is, will above-mentioned no parking indicates
The image of object is paragraphed and/or the process of branch.
Next, the image to above-mentioned no parking marker carries out Character segmentation, then the character after cutting is carried out
Identification finally carries out space of a whole page recovery to the text that identification obtains, according to the relationship of specific Linguistic context, to recognition result into
Row correction.
Fig. 4 is the flow chart of the discrimination method further embodiment of the application illegal parking case, as shown in figure 4, this Shen
Please be in embodiment illustrated in fig. 1, step 104 may include:
Step 401, the integrality of detected vehicle is detected.
Specifically, for the vehicle in detected illegal parking key element, the correlation of above-mentioned vehicle can be checked
It whether not only include headstock in image, but also including the tailstock, if it is, can determine that above-mentioned vehicle is complete；And if above-mentioned vehicle
Associated picture in only headstock or the tailstock, that is assured that above-mentioned vehicle is imperfect.
Step 402, after determining that above-mentioned vehicle is complete, detected no parking marker and above-mentioned vehicle are determined
Positional relationship.
Step 403, according to the positional relationship of above-mentioned no parking marker and above-mentioned vehicle, and forbidding of obtaining of identification
The type of stop sign object determines the violation type of above-mentioned illegal parking case.
Wherein, the type of above-mentioned no parking marker may include: parking restrictions, no parking mark, the road Jin Ting
Section and/or pavement etc..
Step 404, the punishment information progress in the penalty note violation type of above-mentioned illegal parking case and identification obtained
Whether comparison, match according to the violation type of above-mentioned illegal parking case with the punishment information in above-mentioned penalty note, determines above-mentioned disobey
The compliance of method parking case processing.
Specifically, the punishment information in penalty note that the violation type of above-mentioned illegal parking case and identification obtain is carried out pair
Than, needing to combine the relevant regulations in traffic method later, determine in violation type and the above-mentioned penalty note of above-mentioned illegal parking case
Punishment information whether match, if it does, then determining that the processing of above-mentioned illegal parking case meets regulation；If mismatched,
Then determine that the processing of above-mentioned illegal parking case is against regulation.
As an example it is assumed that detecting to the image of illegal parking case, the illegal parking key element of acquisition is vehicle
, the license plate of above-mentioned vehicle, no parking marker and penalty note, further in detected illegal parking key element
Information identified, the license plate number for obtaining above-mentioned license plate is " capital N***** ", and the type of above-mentioned no parking marker is to prohibit
Only parking identifies, and the punishment information in above-mentioned penalty note is 200 yuan of fine.
It is possible, firstly, to detect to the integrality of detected vehicle, checking in the associated picture of above-mentioned vehicle is
No had not only included headstock, but also including the tailstock, if it is, can determine that above-mentioned vehicle is complete；
After determining that above-mentioned vehicle is complete, determine that the position of detected no parking marker and above-mentioned vehicle is closed
System, such as: above-mentioned vehicle is with no parking the distance between marker less than 1 meter；
Further, according to the positional relationship of above-mentioned no parking marker and above-mentioned vehicle, and identification obtain it is upper
Mark that the type for marker of stating that no parking is that no parking, can determine that the violation type of above-mentioned illegal parking case is license plate
Number is the vehicle of " capital N***** " in the region illegal parking that no parking；
Finally, the punishment information in penalty note that the violation type of above-mentioned illegal parking case and identification obtain is carried out pair
Than being exactly here will " license plate number be ' vehicle of capital N***** ' is in the region illegal parking that no parking " and " fine 200
Member " compares, in conjunction with the regulation in traffic method, the violation type for determining above-mentioned illegal parking case and the place in above-mentioned penalty note
Information matches are penalized, and then can determine that the processing of above-mentioned illegal parking case meets regulation.
Fig. 5 is the structural schematic diagram of identification device one embodiment of the application illegal parking case, in the present embodiment
The discrimination method of illegal parking case provided by the embodiments of the present application may be implemented in the identification device of illegal parking case.Such as Fig. 5
It is shown, the identification device of above-mentioned illegal parking case may include: obtain module 51, detection module 52, identification module 53 and really
Cover half block 54；
Wherein, module 51 is obtained, for obtaining the image of illegal parking case；In the present embodiment, to illegal parking case
When being identified, the image sources of illegal parking case can there are many, for example, the image of above-mentioned illegal parking case can
With include: the image at illegal parking scene of traffic administration personnel shooting, candid photograph of deploying to ensure effective monitoring and control of illegal activities illegal parking scene image and/or
The frame image of illegal parking live video.
Wherein, above-mentioned illegal parking live video can be the vehicle parking scene of the shooting of the automobile data recorder on vehicle
Video is also possible to the video at the illegal parking scene of onlooker (driver and passenger in pedestrian or Adjacent vehicles) shooting, may be used also
Be traffic administration personnel shooting illegal parking scene video, the present embodiment is not construed as limiting this.
Detection module 52, for being detected to the illegal parking key element obtained in the image that module 51 obtains；
Identification module 53, for knowing to the information in the detected illegal parking key element of detection module 52
Not；Wherein, above-mentioned illegal parking key element may include following one or combination: vehicle, above-mentioned vehicle license plate, forbid stopping
Car mark object and penalty note；
Information in above-mentioned illegal parking key element may include: the license plate number of above-mentioned license plate, above-mentioned no parking
Punishment information in the type of marker and above-mentioned penalty note.
Wherein, the type of above-mentioned no parking marker may include: parking restrictions, no parking mark, the road Jin Ting
Section and/or pavement etc..
Determining module 54, for according to the detected illegal parking key element of detection module 52, above-mentioned illegal parking
The information in illegal parking key element that positional relationship and the identification of identification module 53 between key element obtain, determines above-mentioned
The compliance of the violation type of illegal parking case and the processing of above-mentioned illegal parking case.
In the identification device of above-mentioned illegal parking case, after obtaining the image that module 51 obtains illegal parking case, inspection
It surveys module 52 to detect the illegal parking key element in above-mentioned image, identification module 53 is to detected illegal parking
Information in key element identified, then determining module 54 according to detected illegal parking key element, above-mentioned disobey
The information in illegal parking key element that positional relationship and identification between method parking key element obtain, determines above-mentioned illegal
The compliance of the violation type for case of stopping and the processing of above-mentioned illegal parking case, intelligently stops to illegal so as to realize
The compliance of vehicle case is identified, and reduces the cost of manual examination and verification, and can enforce the law to traffic administration personnel it is normative into
Row supervision.
Fig. 6 is the structural schematic diagram of another embodiment of the identification device of the application illegal parking case, in the present embodiment,
Detection module 52, specifically for above-mentioned image size and distribution of color be normalized；Utilize depth trained in advance
Neural network model is spent, to the image progress image recognition after normalized, in the image after obtaining above-mentioned normalized
The classification in region and above-mentioned key element where key element, the classification of above-mentioned key element includes following one or combination:
Vehicle, the license plate of above-mentioned vehicle, traffic sign and penalty note.
Specifically, the size of above-mentioned image is normalized in detection module 52, is exactly by the size of above-mentioned image
It is handled according to predefined size, keeps the size of above-mentioned image consistent；Detection module 52 carries out the distribution of color of above-mentioned image
Normalized is to avoid the contrast and/or histogram of above-mentioned image to make the color of above-mentioned image be in same distribution
It is unevenly distributed weighing apparatus.
Further, the identification device of above-mentioned break in traffic rules and regulations case can also include: training module 55；
Training module 55, for utilize training image and the corresponding mark file of above-mentioned training image, to training pattern into
Row training, obtains trained deep neural network model.
In the present embodiment, training module 55 is specifically used for above-mentioned training image and the corresponding mark of above-mentioned training image
File inputs above-mentioned training pattern, is trained using deep neural network algorithm to above-mentioned training pattern；Above-mentioned training image
Corresponding mark file includes the classification in region and above-mentioned key element in above-mentioned training image where key element；When above-mentioned
When error between the result of training pattern output mark file corresponding with above-mentioned training image is less than predetermined threshold, instructed
The deep neural network model perfected.The size of above-mentioned predetermined threshold can in specific implementation, according to system performance and/or reality
Sets itselfs, the present embodiment such as existing demand are not construed as limiting the size of above-mentioned predetermined threshold.
Wherein, above-mentioned deep neural network algorithm can use the multi-target detection recognizer based on deep learning, example
Such as the methods of Fast R-CNN, SSD or YOLO, the present embodiment is not construed as limiting this.
Identification module 53 passes through specifically for being identified by license plate number of the license plate recognition technology to above-mentioned license plate
OCR to above-mentioned traffic sign instruction and above-mentioned penalty note in punishment information identify, the packet in above-mentioned key element
Include following one or combination: the license plate number of above-mentioned license plate, above-mentioned traffic sign instruction and above-mentioned penalty note in punishment information.
Wherein, license plate recognition technology (Vehicle License Plate Recognition；Hereinafter referred to as: VLPR) it is
Video Image identification technology is applied in one of License Plate Identification.License plate recognition technology can be by the licence plate of vehicle
It extracts and identifies from complex background, pass through the skills such as license plate retrieving, image preprocessing, feature extraction, Recognition of License Plate Characters
Art, the information such as identification vehicle identification number, color.
Specifically, firstly, identification module 53 can isolate detected license plate region from above-mentioned image
Come；Then, above-mentioned license plate region is divided into single character again by identification module 53, and Character segmentation generally uses upright projection
Method.Since the inevitable gap location in intercharacter or character of the projection of character in vertical direction obtains the attached of local minimum
Closely, and this position should meet the limitation of character writing format, character, size and some other conditions of license plate, therefore utilize
Vertical projection method, which carries out Character segmentation, preferable effect.
Finally, identification module 53 can be based on template matching algorithm and based on artificial neural network algorithm to the word after segmentation
Symbol is identified.Wherein, it is scaled first by the character binaryzation after segmentation and by its size based on template matching algorithm
The size of template in character database, is then matched with all templates, selects best match as a result.Based on artificial
There are two types of the algorithms of neural network: one is first feature extraction is carried out to character, then training nerve net with obtained feature
Network distributor；Another method is that image is directly inputted network, realizes feature extraction automatically by network until identifying result.
Below to be identified by OCR to the punishment information in the type and above-mentioned penalty note of above-mentioned no parking marker
Process be introduced, following introduction is by taking the type to above-mentioned no parking marker identifies as an example, wherein identification mould
Block 53 is identified by type of the OCR to above-mentioned no parking marker can be with are as follows: identification module 53 is by OCR to forbidding stopping
Fare, pavement and/or taboo are stopped section and are identified.
Specifically, firstly, identification module 53 pre-processes the image of above-mentioned no parking marker, pretreatment is main
It include: binaryzation, noise remove and inclination calibration etc..
Wherein, binaryzation is divided to the content of the image of above-mentioned no parking marker, be divided into foreground information with
Background information, can simply define foreground information is black, and background information is white, and here it is binary pictures；
Noise remove is denoised according to the image of the feature of noise to above-mentioned no parking marker；
Inclination is relatively exactly corrected the direction of the image of above-mentioned no parking marker, avoids above-mentioned no parking mark
The image of will object tilts.
Then, identification module 53 carries out printed page analysis to the image of above-mentioned no parking marker, that is, by above-mentioned taboo
Only the image of stop sign object is paragraphed and/or the process of branch.
Next, identification module 53 carries out Character segmentation to the image of above-mentioned no parking marker, then to cutting after
Character identified, 53 pairs of last identification module identification obtain text carry out space of a whole page recovery, above and below specific language
The relationship of text, is corrected recognition result.
In the present embodiment, determining module 54 may include: that integrity detection submodule 541, positional relationship determine submodule
542, violation type determination module 543 and compliance determine submodule 544；
Wherein, integrity detection submodule 541 is examined for the integrality to the detected vehicle of detection module 52
It surveys；Specifically, for the vehicle in the detected illegal parking key element of detection module 52, integrity detection submodule
541 can check in the associated picture of above-mentioned vehicle whether not only include headstock, but also including the tailstock, if it is, can determine
It is complete to state vehicle；And if there was only headstock or the tailstock in the associated picture of above-mentioned vehicle, that is assured that above-mentioned vehicle is endless
It is whole.
Positional relationship determines submodule, for determining after integrity detection submodule 541 determines that above-mentioned vehicle is complete
The positional relationship of detection module 52 is detected no parking marker and above-mentioned vehicle；
Violation type determination module 543, for the positional relationship according to above-mentioned no parking marker and above-mentioned vehicle,
And the identification of identification module 53 obtains the type of no parking marker, determines the violation type of above-mentioned illegal parking case；
Wherein, the type of above-mentioned no parking marker may include: parking restrictions, no parking mark, prohibit and stop section and/or people
Trade etc..
Compliance determines submodule 544, and the illegal parking case for determining violation type determination module 543 is disobeyed
The punishment information in penalty note that rule type and the identification of identification module 53 obtain compares, according to disobeying for above-mentioned illegal parking case
Whether rule type matches with the punishment information in above-mentioned penalty note, determines the compliance of above-mentioned illegal parking case processing.
Specifically, compliance determines submodule 544 for the violation type of above-mentioned illegal parking case and detected penalizes
After punishment information in list compares, needs to combine the relevant regulations in traffic method, determine above-mentioned illegal parking case
Whether violation type matches with the punishment information in above-mentioned penalty note, if it does, then determining the processing of above-mentioned illegal parking case
Meet regulation；If it does not match, determining that the processing of above-mentioned illegal parking case is against regulation.
As an example it is assumed that detection module 52 detects the image of illegal parking case, the illegal parking of acquisition is closed
Key element is vehicle, the license plate of above-mentioned vehicle, no parking marker and penalty note, and identification module 53 is further to detected
Information in illegal parking key element is identified that the license plate number for obtaining above-mentioned license plate is " capital N***** ", above-mentioned to forbid
The type of stop sign object, which is that no parking, to be identified, and the punishment information in above-mentioned penalty note is 200 yuan of fine.
Firstly, integrity detection submodule 541 can detect the integrality of detected vehicle, check above-mentioned
It whether not only include headstock in the associated picture of vehicle, but also including the tailstock, if it is, can determine that above-mentioned vehicle is complete；
After integrity detection submodule 541 determines that above-mentioned vehicle is complete, positional relationship determines that submodule 542 determines inspection
The positional relationship for obtaining no parking marker and above-mentioned vehicle is surveyed, such as: between above-mentioned vehicle and no parking marker
Distance less than 1 meter；
Further, according to the positional relationship of above-mentioned no parking marker and above-mentioned vehicle, and identification obtain it is upper
Mark that the type for marker of stating that no parking is that no parking, violation type determination module 543 can determine above-mentioned illegal stop
The violation type of vehicle case be license plate number be " capital N***** " vehicle in the region illegal parking that no parking；
Finally, compliance determines the penalty note that submodule 544 obtains the violation type of above-mentioned illegal parking case and identification
In punishment information compare, be exactly here will " license plate number be ' vehicle of capital N***** ' the region that no parking disobey
Method parking " is compared with " 200 yuan of fine ", in conjunction with the regulation in traffic method, determines the violation class of above-mentioned illegal parking case
Punishment information matches in type and above-mentioned penalty note, and then can determine that the processing of above-mentioned illegal parking case meets regulation.
Fig. 7 is the structural schematic diagram of the application computer equipment one embodiment, and above-mentioned computer equipment may include depositing
Reservoir, processor and it is stored in the computer program that can be run on above-mentioned memory and on above-mentioned processor, above-mentioned processor
When executing above-mentioned computer program, the discrimination method of illegal parking case provided by the embodiments of the present application may be implemented.
Wherein, above-mentioned computer equipment can be server, such as: Cloud Server, or electronic equipment, such as:
The intelligent electronic devices such as smart phone, smartwatch or tablet computer, specific form of the present embodiment to above-mentioned computer equipment
It is not construed as limiting.
Fig. 7 shows the block diagram for being suitable for the exemplary computer device 12 for being used to realize the application embodiment.Fig. 7 is shown
Computer equipment 12 be only an example, should not function to the embodiment of the present application and use scope bring any restrictions.
As shown in fig. 7, computer equipment 12 is showed in the form of universal computing device.The component of computer equipment 12 can be with
Including but not limited to: one or more processor or processing unit 16, system storage 28 connect different system components
The bus 18 of (including system storage 28 and processing unit 16).
Bus 18 indicates one of a few class bus structures or a variety of, including memory bus or Memory Controller,
Peripheral bus, graphics acceleration port, processor or the local bus using any bus structures in a variety of bus structures.It lifts
For example, these architectures include but is not limited to industry standard architecture (Industry Standard
Architecture；Hereinafter referred to as: ISA) bus, microchannel architecture (Micro Channel Architecture；Below
Referred to as: MAC) bus, enhanced isa bus, Video Electronics Standards Association (Video Electronics Standards
Association；Hereinafter referred to as: VESA) local bus and peripheral component interconnection (Peripheral Component
Interconnection；Hereinafter referred to as: PCI) bus.
Computer equipment 12 typically comprises a variety of computer system readable media.These media can be it is any can be by
The usable medium that computer equipment 12 accesses, including volatile and non-volatile media, moveable and immovable medium.
System storage 28 may include the computer system readable media of form of volatile memory, such as arbitrary access
Memory (Random Access Memory；Hereinafter referred to as: RAM) 30 and/or cache memory 32.Computer equipment 12
It may further include other removable/nonremovable, volatile/non-volatile computer system storage mediums.Only conduct
Citing, storage system 34 can be used for reading and writing immovable, non-volatile magnetic media, and (Fig. 7 do not show, commonly referred to as " hard disk
Driver ").Although being not shown in Fig. 7, the magnetic for reading and writing to removable non-volatile magnetic disk (such as " floppy disk ") can be provided
Disk drive, and to removable anonvolatile optical disk (such as: compact disc read-only memory (Compact Disc Read Only
Memory；Hereinafter referred to as: CD-ROM), digital multi CD-ROM (Digital Video Disc Read Only
Memory；Hereinafter referred to as: DVD-ROM) or other optical mediums) read-write CD drive.In these cases, each driving
Device can be connected by one or more data media interfaces with bus 18.Memory 28 may include that at least one program produces
Product, the program product have one group of (for example, at least one) program module, and it is each that these program modules are configured to perform the application
The function of embodiment.
Program/utility 40 with one group of (at least one) program module 42 can store in such as memory 28
In, such program module 42 includes --- but being not limited to --- operating system, one or more application program, other programs
It may include the realization of network environment in module and program data, each of these examples or certain combination.Program mould
Block 42 usually executes function and/or method in embodiments described herein.
Computer equipment 12 can also be with one or more external equipments 14 (such as keyboard, sensing equipment, display 24
Deng) communication, can also be enabled a user to one or more equipment interact with the computer equipment 12 communicate, and/or with make
The computer equipment 12 any equipment (such as network interface card, the modulatedemodulate that can be communicated with one or more of the other calculating equipment
Adjust device etc.) communication.This communication can be carried out by input/output (I/O) interface 22.Also, computer equipment 12 may be used also
To pass through network adapter 20 and one or more network (such as local area network (Local Area Network；Hereinafter referred to as:
LAN), wide area network (Wide Area Network；Hereinafter referred to as: WAN) and/or public network, for example, internet) communication.Such as figure
Shown in 7, network adapter 20 is communicated by bus 18 with other modules of computer equipment 12.Although should be understood that in Fig. 7 not
It shows, other hardware and/or software module can be used in conjunction with computer equipment 12, including but not limited to: microcode, equipment are driven
Dynamic device, redundant processing unit, external disk drive array, RAID system, tape drive and data backup storage system etc..
Processing unit 16 by the program that is stored in system storage 28 of operation, thereby executing various function application and
Data processing, such as realize the discrimination method of illegal parking case provided by the embodiments of the present application.
The embodiment of the present application also provides a kind of non-volatile computer readable storage medium storing program for executing, is stored thereon with computer journey
The identification side of illegal parking case provided by the embodiments of the present application may be implemented in sequence, above-mentioned computer program when being executed by processor
Method.
Above-mentioned non-volatile computer readable storage medium storing program for executing can appointing using one or more computer-readable media
Meaning combination.Computer-readable medium can be computer-readable signal media or computer readable storage medium.Computer can
Reading storage medium for example may be-but not limited to-the system of electricity, magnetic, optical, electromagnetic, infrared ray or semiconductor, device
Or device, or any above combination.The more specific example (non exhaustive list) of computer readable storage medium includes:
Electrical connection, portable computer diskette, hard disk, random access memory (RAM), read-only storage with one or more conducting wires
Device (Read Only Memory；Hereinafter referred to as: ROM), erasable programmable read only memory (Erasable
Programmable Read Only Memory；Hereinafter referred to as: EPROM) or flash memory, optical fiber, portable compact disc are read-only deposits
Reservoir (CD-ROM), light storage device, magnetic memory device or above-mentioned any appropriate combination.In this document, computer
Readable storage medium storing program for executing can be any tangible medium for including or store program, which can be commanded execution system, device
Either device use or in connection.
Computer-readable signal media may include in a base band or as carrier wave a part propagate data-signal,
Wherein carry computer-readable program code.The data-signal of this propagation can take various forms, including --- but
It is not limited to --- electromagnetic signal, optical signal or above-mentioned any appropriate combination.Computer-readable signal media can also be
Any computer-readable medium other than computer readable storage medium, which can send, propagate or
Transmission is for by the use of instruction execution system, device or device or program in connection.
The program code for including on computer-readable medium can transmit with any suitable medium, including --- but it is unlimited
In --- wireless, electric wire, optical cable, RF etc. or above-mentioned any appropriate combination.
Can with one or more programming languages or combinations thereof come write for execute the application operation computer
Program code, described program design language include object oriented program language-such as Java, Smalltalk, C++,
It further include conventional procedural programming language-such as " C " language or similar programming language.Program code can be with
It fully executes, partly execute on the user computer on the user computer, being executed as an independent software package, portion
Divide and partially executes or executed on a remote computer or server completely on the remote computer on the user computer.?
It is related in the situation of remote computer, remote computer can pass through the network of any kind --- including local area network (Local
Area Network；Hereinafter referred to as: LAN) or wide area network (Wide Area Network；Hereinafter referred to as: WAN) it is connected to user
Computer, or, it may be connected to outer computer (such as being connected using ISP by internet).
In the description of this specification, reference term " one embodiment ", " some embodiments ", " example ", " specifically show
The description of example " or " some examples " etc. means specific features, structure, material or spy described in conjunction with this embodiment or example
Point is contained at least one embodiment or example of the application.In the present specification, schematic expression of the above terms are not
It must be directed to identical embodiment or example.Moreover, particular features, structures, materials, or characteristics described can be in office
It can be combined in any suitable manner in one or more embodiment or examples.In addition, without conflicting with each other, the skill of this field
Art personnel can tie the feature of different embodiments or examples described in this specification and different embodiments or examples
It closes and combines.
In addition, term " first ", " second " are used for descriptive purposes only and cannot be understood as indicating or suggesting relative importance
Or implicitly indicate the quantity of indicated technical characteristic.Define " first " as a result, the feature of " second " can be expressed or
Implicitly include at least one this feature.In the description of the present application, the meaning of " plurality " is at least two, such as two, three
It is a etc., unless otherwise specifically defined.
Any process described otherwise above or method description are construed as in flow chart or herein, and expression includes
It is one or more for realizing custom logic function or process the step of executable instruction code module, segment or portion
Point, and the range of the preferred embodiment of the application includes other realization, wherein can not press shown or discussed suitable
Sequence, including according to related function by it is basic simultaneously in the way of or in the opposite order, to execute function, this should be by the application
Embodiment person of ordinary skill in the field understood.
Depending on context, word as used in this " if " can be construed to " ... when " or " when ...
When " or " in response to determination " or " in response to detection ".Similarly, depend on context, phrase " if it is determined that " or " if detection
(condition or event of statement) " can be construed to " when determining " or " in response to determination " or " when the detection (condition of statement
Or event) when " or " in response to detection (condition or event of statement) ".
It should be noted that terminal involved in the embodiment of the present application can include but is not limited to personal computer
(Personal Computer；Hereinafter referred to as: PC), personal digital assistant (Personal Digital Assistant；Below
Referred to as: PDA), radio hand-held equipment, tablet computer (Tablet Computer), mobile phone, MP3 player, MP4 player etc..
In several embodiments provided herein, it should be understood that disclosed systems, devices and methods, it can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, for example, multiple units or group
Part can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown
Or the mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, device or unit it is indirect
Coupling or communication connection can be electrical property, mechanical or other forms.
It, can also be in addition, each functional unit in each embodiment of the application can integrate in one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of hardware adds SFU software functional unit.
The above-mentioned integrated unit being realized in the form of SFU software functional unit can store and computer-readable deposit at one
In storage media.Above-mentioned SFU software functional unit is stored in a storage medium, including some instructions are used so that a computer
It is each that device (can be personal computer, server or network equipment etc.) or processor (Processor) execute the application
The part steps of embodiment the method.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (Read-
Only Memory；Hereinafter referred to as: ROM), random access memory (Random Access Memory；Hereinafter referred to as: RAM),
The various media that can store program code such as magnetic or disk.
The foregoing is merely the preferred embodiments of the application, not to limit the application, all essences in the application
Within mind and principle, any modification, equivalent substitution, improvement and etc. done be should be included within the scope of the application protection.
Claims (10)
1. a kind of discrimination method of illegal parking case characterized by comprising
Obtain the image of illegal parking case；
Illegal parking key element in described image is detected；
Information in the detected illegal parking key element is identified；
According to the positional relationship and knowledge between the detected illegal parking key element, the illegal parking key element
The information in the illegal parking key element not obtained determines the violation type of the illegal parking case and described illegal
The compliance of case of stopping processing.
2. the method according to claim 1, wherein the image of the illegal parking case includes: traffic administration
The image at the illegal parking scene of personnel's shooting, the image at the illegal parking scene for candid photograph of deploying to ensure effective monitoring and control of illegal activities and/or illegal parking scene view
The frame image of frequency.
3. method according to claim 1 or 2, which is characterized in that the illegal parking in described image is critical to
Element carries out detection
The size and distribution of color of described image are normalized；
Using deep neural network model trained in advance, image recognition carried out to the image after normalized, described in acquisition
The classification in region and the key element in the image after normalized where key element, the classification of the key element
Including following one or combination: vehicle, the license plate of the vehicle, traffic sign and penalty note.
4. according to the method described in claim 3, it is characterized in that, described using deep neural network model trained in advance,
Before the image progress image recognition after normalized, further includes:
Using training image and the corresponding mark file of the training image, training pattern is trained, is obtained trained
The deep neural network model.
5. according to the method described in claim 4, it is characterized in that, described corresponding using training image and the training image
File is marked, training pattern is trained includes:
The training image and the corresponding mark file of the training image are inputted into the training pattern, utilize depth nerve net
Network algorithm is trained the training pattern；The corresponding mark file of the training image includes crucial in the training image
The classification in region and the key element where element；
When the error between the result of training pattern output mark file corresponding with the training image is less than predetermined threshold
When value, the trained deep neural network model is obtained.
6. according to the method described in claim 3, it is characterized in that, the information in the illegal parking key element includes following
One of or combination: the punishment information in the license plate number of the license plate, the type and the penalty note of no parking the marker；
The information in the detected illegal parking key element carries out identification
It is identified by license plate number of the license plate recognition technology to the license plate, forbids stopping to described by optical character identification
Punishment information in the type of car mark object and the penalty note is identified.
7. according to the method described in claim 6, it is characterized in that, described be critical to according to the detected illegal parking
The letter in the illegal parking key element that positional relationship and identification between plain, the described illegal parking key element obtain
Breath, the compliance for determining that the violation type of the illegal parking case and the illegal parking case are handled include:
The integrality of detected vehicle is detected；
After determining that the vehicle is complete, the positional relationship of detected no parking marker and the vehicle is determined；
According to the positional relationship of no parking the marker and the vehicle, and the identification marker that obtains that no parking
Type determines the violation type of the illegal parking case；
Punishment information in the violation type of the illegal parking case and the penalty note of identification acquisition is compared, according to described
Whether the violation type of illegal parking case matches with the punishment information in the penalty note, determines the illegal parking case processing
Compliance.
8. a kind of identification device of illegal parking case characterized by comprising
Module is obtained, for obtaining the image of illegal parking case；
Detection module, the illegal parking key element in image for obtaining to the acquisition module detect；
Identification module, for being identified to the information in the detected illegal parking key element of the detection module；
Determining module, for crucial according to the detected illegal parking key element of the detection module, the illegal parking
The information in illegal parking key element that positional relationship and identification module identification between element obtain, determines above-mentioned disobey
The violation type of method parking case and the compliance of above-mentioned illegal parking case processing.
9. a kind of computer equipment, which is characterized in that including memory, processor and be stored on the memory and can be in institute
The computer program run on processor is stated, when the processor executes the computer program, is realized as in claim 1-7
Any method.
10. a kind of non-volatile computer readable storage medium storing program for executing, is stored thereon with computer program, which is characterized in that the meter
The method as described in any in claim 1-7 is realized when calculation machine program is executed by processor.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811004600.XA CN109147340A (en) | 2018-08-30 | 2018-08-30 | Discrimination method, device and the computer equipment of illegal parking case |
| PCT/CN2018/123548 WO2020042489A1 (en) | 2018-08-30 | 2018-12-25 | Authentication method and apparatus for illegal parking case, and computer device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811004600.XA CN109147340A (en) | 2018-08-30 | 2018-08-30 | Discrimination method, device and the computer equipment of illegal parking case |
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