WO2022255786A1 - System and method for coping with counterfeit of vehicle license plate - Google Patents

System and method for coping with counterfeit of vehicle license plate Download PDF

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Publication number
WO2022255786A1
WO2022255786A1 PCT/KR2022/007772 KR2022007772W WO2022255786A1 WO 2022255786 A1 WO2022255786 A1 WO 2022255786A1 KR 2022007772 W KR2022007772 W KR 2022007772W WO 2022255786 A1 WO2022255786 A1 WO 2022255786A1
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Prior art keywords
vehicle
license plate
numbers
forgery
vehicle recognition
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PCT/KR2022/007772
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French (fr)
Korean (ko)
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김주영
경왕준
김영진
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주식회사 엠제이비전테크
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Publication of WO2022255786A1 publication Critical patent/WO2022255786A1/en

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    • 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
    • G06V20/63—Scene text, e.g. street names
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
    • G01S19/01—Satellite radio beacon positioning systems transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
    • G01S19/13—Receivers
    • G01S19/14—Receivers specially adapted for specific applications
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/22—Matching criteria, e.g. proximity measures
    • 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
    • G06V10/24—Aligning, centring, orientation detection or correction of the image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/56—Extraction of image or video features relating to colour
    • 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
    • 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
    • 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
    • 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
    • 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
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04N—PICTORIAL COMMUNICATION, e.g. TELEVISION
    • H04N7/00—Television systems
    • H04N7/18—Closed-circuit television [CCTV] systems, i.e. systems in which the video signal is not broadcast
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V2201/00—Indexing scheme relating to image or video recognition or understanding
    • G06V2201/08—Detecting or categorising vehicles

Definitions

  • the present invention relates to a system and method for counterfeiting license plates.
  • An object of the present invention is to provide a vehicle license plate forgery countermeasure system and method that can solve the conventional problems.
  • License plate forgery response system for solving the above problem inputs image information of the front and rear license plates of the vehicle photographed by the camera unit, and then recognizes the character strings and numbers in the front and rear license plates After, a plurality of vehicle recognition modules installed in different places to provide a determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and identical; and registering the GPS information of the plurality of vehicle recognition modules and the determination result provided by each vehicle recognition module, and detecting the same vehicle number in different places within a predetermined time based on the determination result provided by the plurality of vehicle recognition modules; Alternatively, if the front and rear license plates are different or the license plate is unregistered, a license plate forgery response server that determines the amount of license plate counterfeiting of the vehicle and reports the judgment result to the competent authority.
  • the plurality of vehicle recognition modules include a vehicle registration unit for registering a vehicle number, color, model, and year; A camera unit for taking pictures of the front and rear of the vehicle; an image identification unit for identifying vehicle color, vehicle shape, number and character string in the front image and rear image captured by the camera unit; an identity determination unit that determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit; and a communication unit that transmits the location information of the vehicle recognition module, the determination result of the identity determination unit, and the registration information of the vehicle registration unit to the license plate forgery response server.
  • a vehicle registration unit for registering a vehicle number, color, model, and year
  • a camera unit for taking pictures of the front and rear of the vehicle
  • an image identification unit for identifying vehicle color, vehicle shape, number and character string in the front image and rear image captured by the camera unit
  • an identity determination unit that determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit
  • a communication unit that
  • the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B occur between It is characterized in that the moving distance and average speed are calculated through the car, and the realistic possibility of movement is determined based on the calculated moving distance and average speed.
  • the license plate counterfeiting countermeasure method for solving the above problems, after inputting image information of the front and rear license plates of the vehicle photographed by the camera unit in each of a plurality of vehicle recognition modules installed in different places, After recognizing the character strings and numbers in the front and rear license plates, providing a determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and identical;
  • the license plate forgery response server registers the GPS information of the plurality of vehicle recognition modules and the determination result provided from each vehicle recognition module, and the same vehicle in different places within a predetermined time based on the determination result provided from the plurality of vehicle recognition modules. If the number is detected, the front and rear license plates are different, or the license plate is unregistered, determining whether the vehicle is falsified license plate; and reporting the judgment result to the competent authority.
  • the step of determining is the difference between the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module of region B when the same license plate number is detected in different regions within a preset time. It is characterized in that it is a step of calculating the moving distance and average speed through and determining the realistic possibility of movement based on the calculated moving distance and average speed.
  • the determining step further comprises determining the mobility by applying a road traffic volume between area A and area B generated during a predetermined time period when determining the mobility. .
  • the person in charge automatically determines whether the vehicle number is forged by visually checking the vehicle number with the naked eye.
  • the accuracy of detecting counterfeit vehicles and the time required for this can be minimized.
  • FIG. 1 is a network configuration diagram of a vehicle license plate counterfeiting system according to an embodiment of the present invention.
  • FIG. 2 is a device configuration diagram of the vehicle recognition module shown in FIG. 1 .
  • FIG. 3 is a device configuration diagram of a vehicle number counterfeiting countermeasure server shown in FIG. 1. Referring to FIG. 3
  • FIG. 4 is a flowchart illustrating a method for coping with vehicle license plate counterfeiting according to an embodiment of the present invention.
  • FIG. 5 is a diagram of an example computing environment in which one or more embodiments set forth herein may be implemented.
  • first and second may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.
  • FIG. 1 is a device configuration diagram of a vehicle license plate forgery response system according to an embodiment of the present invention.
  • the vehicle license plate forgery response system 100 includes a plurality of license plate recognition modules 110 and a license plate counterfeiting server 120 .
  • the plurality of vehicle recognition modules 110 input image information of the front and rear license plates of the vehicle photographed by the camera unit, recognize strings and numbers in the front and rear license plates, and then input the letters and numbers of the front license plates and It is characterized in that it is installed in different places to provide the result of determining whether the letters and numbers of the rear license plate are registered and whether they are identical.
  • the license plate recognition module 110 can be installed in any space where a vehicle can pass or be parked, such as a parking lot, a back road, or a shoulder.
  • the vehicle recognition module 110 includes a vehicle registration unit 111, a camera unit 112, an image identification unit 113, an identity determination unit 114 and a communication unit 115.
  • the vehicle registration unit 111 may be configured to register a vehicle number, color, vehicle model, and model year.
  • the vehicle registration unit 111 may receive and register vehicle information entering and exiting a corresponding parking area from a terminal of a parking attendant in charge of a parking area within a building.
  • the camera unit 112 is composed of a pair of cameras and may be configured to capture front and rear images of the vehicle.
  • the pair of cameras can adjust the distance ratio according to the far and near distances to convert them into images from which distortion in each area is removed.
  • a method for correcting such distorted image information forward mapping through application of a correction coefficient and An interpolation method accompanying this may be used, and inverse mapping, which is a method of finding matching points of the distorted image assuming a pre-corrected image, may be used.
  • the image identification unit 113 may be configured to identify vehicle color, vehicle shape, number, and character string in the front image and rear image captured by the camera unit 112 .
  • the image identification unit 113 extracts a plurality of vehicle regions included in the image, extracts license plate regions through parallel processing without performing binarization on the extracted vehicle regions, and then vehicles. You can even recognize the number.
  • the shape of the object is extracted after separating the foreground pixel and the background pixel from the Gaussian distribution of each pixel using the data of the previous frame and the data of the current frame, and at this time object detection
  • the object shape is extracted by performing the calculation process of the algorithm.
  • the object detection algorithm converts the input image to a preset size (e.g., 640x480), compares pixels of the previous frame data with pixel data of the current frame to determine a foreground pixel or background pixel, and separates the foreground pixel to determine the size of the object.
  • a process of extracting a form can be performed. That is, after converting the input image to a preset size, calculating the covariance of the color values (R, G, B) of each pixel and separating them into K Gaussian distributions, determining the foreground pixel and the background pixel, The object shape is extracted by extracting the outline from the binarized image separated by the background pixels.
  • an object detection algorithm is performed to determine an object predicted to be a vehicle based on object size information for object shapes detected from the current frame, extract a region including the predicted vehicle as a vehicle region, and After horizontally scanning the vehicle in the separated vehicle area and extracting the license plate candidate area, the license plate is recognized by separating letters and numbers by applying the contour algorithm and chain code algorithm.
  • processing speed when a plurality of vehicles exist in a high-resolution image may be improved by performing number recognition calculation processing for the extracted vehicle areas in parallel.
  • the number recognition algorithm for performing the number recognition operation process on the vehicle areas extracted from the current frame is initialized so that the above-described number recognition operation process is parallelly processed corresponding to each extracted vehicle area.
  • the number recognition algorithm initialized to each vehicle area by performing the number recognition operation process on the extracted vehicle areas so that the number recognition operation process on the extracted vehicle areas can be performed in parallel is only for the corresponding area.
  • the vehicle number is recognized and output.
  • the identity determination unit 114 determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit 113 .
  • the communication unit 114 transmits the location information of the vehicle recognition module, the determination result of the identity determination unit, and the registration information of the vehicle registration unit to a license plate counterfeiting server to be described later.
  • the communication unit 114 may communicate with the vehicle number counterfeit server through a network, and the network may include RF, a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, and a 5rd Generation Partnership Project (5GPP) network.
  • 3GPP 3rd Generation Partnership Project
  • LTE Long Term Evolution
  • 5GPP 5rd Generation Partnership Project
  • WIMAX World Interoperability for Microwave Access
  • Internet Internet
  • LAN Local Area Network
  • Wireless LAN Wireless Local Area Network
  • WAN Wide Area Network
  • PAN Personal Area Network
  • Bluetooth Bluetooth
  • a network an NFC network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, and the like are included, but are not limited thereto.
  • DMB Digital Multimedia Broadcasting
  • the term at least one is defined as a term including singular and plural, and even if at least one term does not exist, each component may exist in singular or plural, and may mean singular or plural. It will be self-evident. In addition, the singular or plural number of each component may be changed according to embodiments.
  • the license plate counterfeiting server 120 registers the GPS information of the plurality of vehicle recognition modules 110 and the determination result provided by each vehicle recognition module, and the plurality of vehicle recognition modules 110 Based on the determination result provided by the module, if the same vehicle number is detected in different places within a predetermined time, or if the front and rear license plates are different or unregistered license plates, the vehicle is judged for counterfeit license plate and then the determination result is It may be configured to report to the competent authority.
  • the license plate forgery response server 120 detects the same license plate number in different regions within a predetermined time, the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B The moving distance and average speed are calculated through the car, and the realistic possibility of movement is determined based on the calculated moving distance and average speed.
  • the license plate counterfeiting server 120 determines the mobility by applying the road traffic volume between area A and area B generated during a preset time period.
  • the license plate forgery response server 120 includes a vehicle information registration unit 121, a vehicle number comparison and determination unit 122, a moving distance and average speed calculation unit 123, a counterfeit vehicle quantity determination unit 124 and a report section 125.
  • the vehicle information registration unit 121 may be configured to register the GPS information of the vehicle recognition location transmitted from the vehicle recognition module 110 and the determination result (vehicle identification result) by time slot.
  • the vehicle number comparison and determination unit 122 determines whether the vehicle numbers transmitted from the vehicle number recognition modules located in different places are identical.
  • the moving distance and average speed calculation unit 123 calculates the vehicle number recognition time of the vehicle recognition module in region A and the vehicle recognition module in region B.
  • the moving distance and average speed are calculated through the difference between vehicle number recognition times.
  • the counterfeit distance determination unit 124 determines the actual possibility of movement based on the moving distance and average speed calculated by the moving distance and average speed calculating unit 123 .
  • the realistic movement distance means the possibility of movement within a physically preset time.
  • counterfeit vehicle weight determination unit 124 may determine realistic mobility by applying the road traffic volume between regions A and B generated during a preset time from an external server.
  • the report unit 125 transmits the license plate number of the vehicle determined to be a counterfeit vehicle in the counterfeit vehicle determination unit 124 and the GPS information of the place recognized as a counterfeit vehicle to the competent authority.
  • FIG. 4 is a flowchart illustrating a method for coping with license plate counterfeiting according to an embodiment of the present invention.
  • the vehicle license plate forgery counterfeiting method (S700) according to an embodiment of the present invention, image information of front and rear license plates of a vehicle photographed by a camera unit is obtained from each of a plurality of vehicle recognition modules installed in different places. After inputting, after recognizing the character strings and numbers in the front and rear license plates, if the determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and whether they are identical is provided (S710), the vehicle license plate The forgery response server 120 registers the GPS information of the plurality of vehicle recognition modules and the determination result provided from each vehicle recognition module, and at different places within a predetermined time based on the determination result provided from the plurality of vehicle recognition modules. When the same vehicle number is detected, or the front and rear license plates are different, or unregistered license plates are detected, the vehicle is judged to be a counterfeit license plate (S720).
  • the license plate corresponding server 120 reports the judgment result to the competent authority (S730).
  • step S720 when the same license plate number is detected in different regions within a predetermined time, the moving distance through the difference between the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B and a process of calculating an average speed and determining a realistic possibility of movement based on the calculated moving distance and average speed.
  • step S720 when determining the mobility, the possibility of mobility may be determined by applying the road traffic volume between area A and area B generated during a preset time period.
  • the person in charge automatically determines whether the vehicle number is forged by visually checking the vehicle number with the naked eye.
  • the accuracy of detecting counterfeit vehicles and the time required for this can be minimized.
  • computing device 1100 may be a personal computer, server computer, handheld or laptop device, mobile device (mobile phone, personal digital assistant, media player, etc.), multiprocessor system, consumer electronics, mini computer, mainframe computer, distributed computing environments that include any of the foregoing systems or devices; and the like.
  • Computing device 1100 may include at least one processing unit 1110 and memory 1120 .
  • the processing unit 1110 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and the like. and may have a plurality of cores.
  • the memory 1120 may be volatile memory (eg, RAM, etc.), non-volatile memory (eg, ROM, flash memory, etc.), or a combination thereof.
  • computing device 1100 may include additional storage 1130 .
  • Storage 1130 includes, but is not limited to, magnetic storage, optical storage, and the like.
  • the storage 1130 may store computer readable instructions for implementing one or more embodiments disclosed herein, and may also store other computer readable instructions for implementing an operating system, application programs, and the like. Computer readable instructions stored in storage 1130 may be loaded into memory 1120 for execution by processing unit 1110 .
  • Computing device 1100 can also include input device(s) 1140 and output device(s) 1150 .
  • input device(s) 1140 may include, for example, a keyboard, mouse, pen, voice input device, touch input device, infrared camera, video input device, or any other input device.
  • Output device(s) 1150 may also include, for example, one or more displays, speakers, printers, or any other output devices, or the like. Additionally, computing device 1100 may use an input device or output device included in another computing device as input device(s) 1140 or output device(s) 1150 .
  • Computing device 1100 may also include communication connection(s) 1160 that allow computing device 1100 to communicate with other devices (eg, computing device 1300).
  • communication connection(s) 1160 that allow computing device 1100 to communicate with other devices (eg, computing device 1300).
  • communication connection(s) 1160 may be a modem, network interface card (NIC), integrated network interface, radio frequency transmitter/receiver, infrared port, USB connection, or other device for connecting computing device 1100 to other computing devices. May contain interfaces. Further, communication connection(s) 1160 may include a wired connection or a wireless connection. Each component of the aforementioned computing device 1100 may be connected by various interconnections such as a bus (eg, peripheral component interconnection (PCI), USB, firmware (IEEE 1394), optical bus structure, etc.) and may be interconnected by the network 1200.
  • PCI peripheral component interconnection
  • IEEE 1394 firmware
  • optical bus structure etc.
  • a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer.
  • both the application running on the controller and the controller may be components.
  • One or more components can reside within a process and/or thread of execution and a component can be localized on one computer or distributed between two or more computers.

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Abstract

A system for coping with a counterfeit of a vehicle license plate, according to an embodiment of the present invention, comprises: a plurality of vehicle recognition modules that are installed at different locations, input information regarding images of front and rear license plates of a vehicle captured by a camera unit, recognize character strings and numbers within the front and rear license plates, and then provide determination results of determining whether or not characters and numbers on the front license plate, and characters and numbers on the rear license plate have been registered and whether or not the characters and numbers on the front license plate are identical to the characters and numbers on the rear license plate; and a license plate counterfeit coping server that registers GPS information of the plurality of vehicle recognition modules and the determination results provided by the respective vehicle recognition modules, if, on the basis of the determination results provided by the plurality of vehicle recognition modules, the same vehicle number is detected at different locations within a preset time, front and rear license plates are different from each other, or a license plate is an unregistered license plate, determines a corresponding vehicle as a vehicle having a counterfeited license plate, and then reports the result of the determination to a competent authority.

Description

차량번호판 위조 대응 시스템 및 방법Vehicle license plate forgery countermeasure system and method
본 발명은 차량번호판 위조 대응 시스템 및 방법에 관한 것이다.The present invention relates to a system and method for counterfeiting license plates.
종래의 차량 인식(차량 감시)은 우리가 알고 있는 바와 마찬가지로 주요 도로에 설치된 감시 카메라를 통해 할 수 있었으며, 경찰이나 감시원이 눈으로 차량 번호, 색깔 등을 확인 후, 무전으로 이를 확인하는 방식으로 할 수도 있었다.Conventional vehicle recognition (vehicle surveillance), as we know, could be done through surveillance cameras installed on major roads, and police or guards can check the vehicle number and color with their eyes, and then check it by radio. could have been
그러나, 이러한 방식으로 차량을 확인한다는 것은 어느 정도의 한계를 인정할 수 밖에 없는 문제점이 있었다. 즉, 주요 도로에 설치된 감시 카메라는 그 위치가 고정적이라 상당수가 노출되어 있다는 문제점을 가지고 있을 뿐만 아니라, 해당 위치에서 문제 차량을 감시 카메라로 포착한다 하여도 이를 통한 해당 차량의 검거로 연결시키기에는 시간이 많이 필요하고, 검거 요원과 문제 차량의 거리가 떨어져 있는 문제점이 있었다.However, checking the vehicle in this way has a problem in that there are certain limitations. In other words, surveillance cameras installed on major roads not only have a problem that many of them are exposed because their locations are fixed, but even if a surveillance camera captures a vehicle in question at that location, it takes too much time to connect it to the arrest of the vehicle. There was a problem that a lot of this was needed, and the distance between the arresting agent and the vehicle in question was far.
또한, 도로, 경찰, 단속 요원 등 사람이 직접 감시를 하는 경우에 있어서는 기본적으로 지속적인 감시가 불가능할 뿐만 아니라, 감시 기간 내에도 완벽한 감시를 하기가 어렵다는 문제점이 있었다.In addition, in the case of direct monitoring by people such as roads, police, and enforcement personnel, there is a problem that not only continuous monitoring is basically impossible, but also difficult to complete monitoring during the monitoring period.
또한, 순간적인 차량의 발견시에 해당 차량이 문제 차량인지 파악하기도 어렵고, 문제 차량을 발견하여도 자료로 남기기 어려워 실제 활용이 쉽지 않다는 문제점이 있었다.In addition, when a vehicle is discovered momentarily, it is difficult to determine whether the vehicle is a problem vehicle, and even if a vehicle is discovered, it is difficult to leave it as data, making it difficult to actually utilize it.
[선행기술문헌][Prior art literature]
한국공개특허공보 제10-2003-0009149호Korean Patent Publication No. 10-2003-0009149
본 발명이 해결하고자 하는 과제는 종래의 문제점을 해결할 수 있는 차량 번호판 위조 대응 시스템 및 방법을 제공하는 데 그 목적이 있다.An object of the present invention is to provide a vehicle license plate forgery countermeasure system and method that can solve the conventional problems.
상기 과제를 해결하기 위한 본 발명의 일 실시예에 따른 번호판 위조 대응 시스템은 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공하는 서로 다른 장소에 설치된 복수의 차량인식 모듈; 및 상기 복수의 차량인식 모듈의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단한 후, 판단결과를 관할기관에 신고하는 번호판 위조 대응 서버를 포함한다.License plate forgery response system according to an embodiment of the present invention for solving the above problem inputs image information of the front and rear license plates of the vehicle photographed by the camera unit, and then recognizes the character strings and numbers in the front and rear license plates After, a plurality of vehicle recognition modules installed in different places to provide a determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and identical; and registering the GPS information of the plurality of vehicle recognition modules and the determination result provided by each vehicle recognition module, and detecting the same vehicle number in different places within a predetermined time based on the determination result provided by the plurality of vehicle recognition modules; Alternatively, if the front and rear license plates are different or the license plate is unregistered, a license plate forgery response server that determines the amount of license plate counterfeiting of the vehicle and reports the judgment result to the competent authority.
일 실시예에서, 상기 복수의 차량인식 모듈은 차량번호, 색상, 차종, 연식을 등록한 차량등록부; 차량의 전면 및 후면을 촬영하는 카메라부; 상기 카메라부에서 촬영된 전면 이미지 및 후면 이미지 내의 차량색상, 차량모양, 숫자 및 문자열을 식별하는 이미지 식별부; 상기 이미지 식별부에서 식별된 차량의 색상, 모양, 전면 번호 및 후면 번호의 동일성을 판단하는 동일성 판단부; 및 상기 차량인식 모듈의 위치정보, 상기 동일성 판단부의 판단결과 및 차량등록부의 등록정보를 상기 번호판 위조 대응 서버로 송출하는 통신부를 포함한다.In one embodiment, the plurality of vehicle recognition modules include a vehicle registration unit for registering a vehicle number, color, model, and year; A camera unit for taking pictures of the front and rear of the vehicle; an image identification unit for identifying vehicle color, vehicle shape, number and character string in the front image and rear image captured by the camera unit; an identity determination unit that determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit; and a communication unit that transmits the location information of the vehicle recognition module, the determination result of the identity determination unit, and the registration information of the vehicle registration unit to the license plate forgery response server.
일 실시예에서, 상기 번호판 위조 대응서버는 기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단하는 것을 특징으로 한다.In one embodiment, when the license plate counterfeiting response server detects the same license plate number in different regions within a predetermined time, the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B occur between It is characterized in that the moving distance and average speed are calculated through the car, and the realistic possibility of movement is determined based on the calculated moving distance and average speed.
상기 과제를 해결하기 위한 본 발명의 일 실시예에 따른 번호판 위조 대응 방법은 서로 다른 장소에 설치된 복수의 차량인식 모듈 각각에서 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공하는 단계; 번호판 위조 대응 서버에서 상기 복수의 차량인식 모듈의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단하는 단계; 및 판단결과를 관할기관에 신고하는 단계를 포함한다.In the license plate counterfeiting countermeasure method according to an embodiment of the present invention for solving the above problems, after inputting image information of the front and rear license plates of the vehicle photographed by the camera unit in each of a plurality of vehicle recognition modules installed in different places, After recognizing the character strings and numbers in the front and rear license plates, providing a determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and identical; The license plate forgery response server registers the GPS information of the plurality of vehicle recognition modules and the determination result provided from each vehicle recognition module, and the same vehicle in different places within a predetermined time based on the determination result provided from the plurality of vehicle recognition modules. If the number is detected, the front and rear license plates are different, or the license plate is unregistered, determining whether the vehicle is falsified license plate; and reporting the judgment result to the competent authority.
일 실시예에서, 상기 판단하는 단계는 기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단하는 단계인 것을 특징으로 한다.In one embodiment, the step of determining is the difference between the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module of region B when the same license plate number is detected in different regions within a preset time. It is characterized in that it is a step of calculating the moving distance and average speed through and determining the realistic possibility of movement based on the calculated moving distance and average speed.
일 실시예에서, 상기 판단하는 단계는 상기 이동가능성을 판단시에, 기 설정되 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 이동가능성을 판단하는 단계를 더 포함하는 것을 특징으로 한다.In one embodiment, the determining step further comprises determining the mobility by applying a road traffic volume between area A and area B generated during a predetermined time period when determining the mobility. .
따라서, 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템 및 방법을 이용하면, 종래의 차량번호 위조차량에 대해서 담당자가 육안으로 차량번호를 육안으로 확인하던 방식으로 자동으로 위조 여부를 판단하게 함으로서, 위조 차량의 적발성에 대한 정확성 및 이에 소요되는 시간을 최소화시킬 수 있다는 이점이 있다.Therefore, using the vehicle license plate forgery countermeasure system and method according to an embodiment of the present invention, for conventional vehicle number counterfeit vehicles, the person in charge automatically determines whether the vehicle number is forged by visually checking the vehicle number with the naked eye. However, there is an advantage in that the accuracy of detecting counterfeit vehicles and the time required for this can be minimized.
상술한 이점을 통해 교통위법 단속 주기관과 실시간 연동함으로써 전국 각지에 불법으로 시행되는 차량번호 위조 차량을 동시다발적으로 적발할 수 있다는 이점이 있다.Through the above-mentioned advantages, there is an advantage in that it is possible to simultaneously detect counterfeit license plate vehicles illegally implemented in various parts of the country by linking with the traffic violation control agency in real time.
도 1은 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템의 네트워크 구성도이다.1 is a network configuration diagram of a vehicle license plate counterfeiting system according to an embodiment of the present invention.
도 2는 도 1에 도시된 차량인식 모듈의 장치 구성도이다.FIG. 2 is a device configuration diagram of the vehicle recognition module shown in FIG. 1 .
도 3은 도 1에 도시된 차량번호 위조 대응서버의 장치 구성도이다.FIG. 3 is a device configuration diagram of a vehicle number counterfeiting countermeasure server shown in FIG. 1. Referring to FIG.
도 4는 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 방법을 설명한 흐름도이다.4 is a flowchart illustrating a method for coping with vehicle license plate counterfeiting according to an embodiment of the present invention.
도 5는 본 명세서에 개진된 하나 이상의 실시예가 구현될 수 있는 예시적인 컴퓨팅 환경을 도시한 도이다.5 is a diagram of an example computing environment in which one or more embodiments set forth herein may be implemented.
본 발명은 다양한 변경을 가할 수 있고 여러 가지 형태를 가질 수 있는바, 특정 실시예들을 도면에 예시하고 본문에 상세하게 설명하고자 한다. 그러나, 이는 본 발명을 특정한 개시 형태에 대해 한정하려는 것이 아니며, 본 발명의 사상 및 기술 범위에 포함되는 모든 변경, 균등물 내지 대체물을 포함하는 것으로 이해되어야 한다.Since the present invention can have various changes and various forms, specific embodiments will be illustrated in the drawings and described in detail in the text. However, it should be understood that this is not intended to limit the present invention to the specific disclosed form, and includes all modifications, equivalents, and substitutes included in the spirit and scope of the present invention.
제1, 제2 등의 용어는 다양한 구성 요소들을 설명하는데 사용될 수 있지만, 상기 구성 요소들은 상기 용어들에 의해 한정되어서는 안 된다. 상기 용어들은 하나의 구성 요소를 다른 구성 요소로부터 구별하는 목적으로만 사용된다. 예를 들어, 본 발명의 권리 범위를 벗어나지 않으면서 제1 구성 요소는 제2 구성 요소로 명명될 수 있고, 유사하게 제2 구성 요소도 제1 구성 요소로 명명될 수 있다. Terms such as first and second may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention.
본 출원에서 사용한 용어는 단지 특정한 실시예들을 설명하기 위해 사용된 것으로, 본 발명을 한정하려는 의도가 아니다. 단수의 표현은 문맥상 명백하게 다르게 뜻하지 않는 한, 복수의 표현을 포함한다. 본 출원에서, "포함하다" 또는 "가지다" 등의 용어는 명세서에 기재된 특징, 숫자, 단계, 동작, 구성 요소, 부분품 또는 이들을 조합한 것이 존재함을 지정하려는 것이지, 하나 또는 그 이상의 다른 특징들이나 숫자, 단계, 동작, 구성 요소, 부분품 또는 이들을 조합한 것들의 존재 또는 부가 가능성을 미리 배제하지 않는 것으로 이해되어야 한다.Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "comprise" or "having" are intended to indicate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features or It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.
다르게 정의되지 않는 한, 기술적이거나 과학적인 용어를 포함해서 여기서 사용되는 모든 용어들은 본 발명이 속하는 기술 분야에서 통상의 지식을 가진 자에 의해 일반적으로 이해되는 것과 동일한 의미를 가진다.Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the present invention belongs.
일반적으로 사용되는 사전에 정의되어 있는 것과 같은 용어들은 관련 기술의 문맥상 가지는 의미와 일치하는 의미를 갖는 것으로 해석되어야 하며, 본 출원에서 명백하게 정의하지 않는 한, 이상적이거나 과도하게 형식적인 의미로 해석되지 않는다.Terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with the meaning in the context of the related art, and unless explicitly defined in the present application, they should not be interpreted in an ideal or excessively formal meaning. don't
이하, 첨부된 도면들에 기초하여 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템을 보다 상세하게 설명하도록 한다.Hereinafter, a vehicle license plate forgery response system according to an embodiment of the present invention will be described in more detail based on the accompanying drawings.
도 1은 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템의 장치 구성도이다.1 is a device configuration diagram of a vehicle license plate forgery response system according to an embodiment of the present invention.
도 1에 도시된 바와 같이, 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템(100)은 복수의 차량번호 인식모듈(110) 및 차량번호 위조 대응서버(120)를 포함한다.As shown in FIG. 1 , the vehicle license plate forgery response system 100 according to an embodiment of the present invention includes a plurality of license plate recognition modules 110 and a license plate counterfeiting server 120 .
상기 복수의 차량인식 모듈(110)은 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공하는 서로 다른 장소에 설치된 것을 특징으로 한다.The plurality of vehicle recognition modules 110 input image information of the front and rear license plates of the vehicle photographed by the camera unit, recognize strings and numbers in the front and rear license plates, and then input the letters and numbers of the front license plates and It is characterized in that it is installed in different places to provide the result of determining whether the letters and numbers of the rear license plate are registered and whether they are identical.
상기 차량번호 인식모듈(110)은 주차장, 이면도로, 갓길 등 차량이 지나다니거나 또는 주차될 수 있는 모든 공간에 설치가능하다.The license plate recognition module 110 can be installed in any space where a vehicle can pass or be parked, such as a parking lot, a back road, or a shoulder.
보다 구체적으로, 차량인식 모듈(110)은 차량등록부(111), 카메라부(112), 이미지 식별부(113), 동일성 판단부(114) 및 통신부(115)를 포함한다.More specifically, the vehicle recognition module 110 includes a vehicle registration unit 111, a camera unit 112, an image identification unit 113, an identity determination unit 114 and a communication unit 115.
상기 차량등록부(111)은 차량번호, 색상, 차종, 연식을 등록하는 구성일 수 있다. 상기 차량등록부(111)은 건물 내의 주차지역을 관할하는 주차요원의 단말로부터 해당 주차지역에 출입하는 차량정보를 입력받아 등록할 수 있다.The vehicle registration unit 111 may be configured to register a vehicle number, color, vehicle model, and model year. The vehicle registration unit 111 may receive and register vehicle information entering and exiting a corresponding parking area from a terminal of a parking attendant in charge of a parking area within a building.
상기 카메라부(112)는 한쌍의 카메라로 구성되며, 차량의 전면 및 후면 이미지를 촬영하는 구성일 수 있다. The camera unit 112 is composed of a pair of cameras and may be configured to capture front and rear images of the vehicle.
상기 한쌍의 카메라는 원거리 및 근거리에 따른 거리 비율을 조정하여 각 영역의 왜곡을 제거한 영상으로 변환시킬 수 있고, 이러한 왜곡된 영상정보의 보정방법으로 보정계수의 적용을 통한 포워드 맵핑(forward mapping)과 이에 부수되는 보간법(interpolation)이 활용될 수 있으며, 미리 보정된 영상을 가정하고 이 영상의 점들이 왜곡 영상의 어느 점과 매칭이 되는지 찾는 방법인 인버스 맵핑(inverse mapping)이 사용될 수도 있다.The pair of cameras can adjust the distance ratio according to the far and near distances to convert them into images from which distortion in each area is removed. As a method for correcting such distorted image information, forward mapping through application of a correction coefficient and An interpolation method accompanying this may be used, and inverse mapping, which is a method of finding matching points of the distorted image assuming a pre-corrected image, may be used.
상기 이미지 식별부(113)는 상기 카메라부(112)에서 촬영된 전면 이미지 및 후면 이미지 내의 차량색상, 차량모양, 숫자 및 문자열을 식별하는 구성일 수 있다.The image identification unit 113 may be configured to identify vehicle color, vehicle shape, number, and character string in the front image and rear image captured by the camera unit 112 .
상기 이미지 식별부(113)는 전면 이미지 내의 복수의 차량이 포함될 경우, 이미지에 포함되는 다수의 차량영역을 추출하고, 추출된 차량영역에 대하여 이진화를 수행함 없이 병렬처리에 의해 번호판 영역을 추출한 후 차량번호를 인식할 수도 있다.When a plurality of vehicles are included in the front image, the image identification unit 113 extracts a plurality of vehicle regions included in the image, extracts license plate regions through parallel processing without performing binarization on the extracted vehicle regions, and then vehicles. You can even recognize the number.
예컨대, 1초당 n 프레임을 가지는 입력된 이미지 데이터로부터 이전 프레임의 데이터와 현재 프레임의 데이터를 이용하여 각 픽셀의 가우시안 분포로부터 전경 픽셀과 배경 픽셀을 분리한 후 물체의 형태를 추출하고, 이때 객체 검출 알고리즘의 연산처리를 수행하여 물체형태를 추출한다.For example, from the input image data having n frames per second, the shape of the object is extracted after separating the foreground pixel and the background pixel from the Gaussian distribution of each pixel using the data of the previous frame and the data of the current frame, and at this time object detection The object shape is extracted by performing the calculation process of the algorithm.
객체 검출 알고리즘은 입력된 영상을 기 설정된 크기(예, 640x480)로 변환한 후, 이전 프레임 데이터의 픽셀과 현재 프레임 픽셀 데이터를 비교하여 전경 픽셀 또는 배경 픽셀을 판단하고, 전경 픽셀을 분리하여 물체의 형태를 추출하는 처리과정을 수행할 수 있다. 즉, 입력된 이미지를 기 설정된 크기로 변환한 후 각 픽셀의 색상 값(R, G, B)들의 공분산을 계산하여 K 개의 가우시안 분포로 분리한 후 전경 픽셀과 배경 픽셀을 결정하고, 전경 픽셀과 배경 픽셀로 분리된 이진화된 영상으로부터 윤곽선을 추출하여 물체형태를 추출한다.The object detection algorithm converts the input image to a preset size (e.g., 640x480), compares pixels of the previous frame data with pixel data of the current frame to determine a foreground pixel or background pixel, and separates the foreground pixel to determine the size of the object. A process of extracting a form can be performed. That is, after converting the input image to a preset size, calculating the covariance of the color values (R, G, B) of each pixel and separating them into K Gaussian distributions, determining the foreground pixel and the background pixel, The object shape is extracted by extracting the outline from the binarized image separated by the background pixels.
이후, 객체 검출 알고리즘이 수행되어 현재 프레임으로부터 검출된 물체형태들에 대하여, 물체의 크기 정보를 기준으로 차량으로 예측되는 물체를 판단하고, 예측된 차량을 포함하는 영역을 차량영역으로 추출하고, 상기 분리된 차량영역에서 차량을 수평 스캔하여 번호판후보 영역을 추출한 후, 컨투어 알고리즘과 체인코드 알고리즘을 적용하여 문자와 숫자를 분리하여 번호판을 인식한다. 이때, 추출된 상기 차량영역들에 대한 번호인식 연산처리를 병렬로 수행하는 것에 의해, 고해상도 영상에서 다수개의 차량이 존재하는 경우의 처리속도를 개선할 수 있다.Thereafter, an object detection algorithm is performed to determine an object predicted to be a vehicle based on object size information for object shapes detected from the current frame, extract a region including the predicted vehicle as a vehicle region, and After horizontally scanning the vehicle in the separated vehicle area and extracting the license plate candidate area, the license plate is recognized by separating letters and numbers by applying the contour algorithm and chain code algorithm. In this case, processing speed when a plurality of vehicles exist in a high-resolution image may be improved by performing number recognition calculation processing for the extracted vehicle areas in parallel.
한편, 상술한 번호인식 연산처리가 추출된 차량영역 각각에 대응하여 병렬 처리되도록 현재 프레임으로부터 추출된 차량영역들로 번호인식 연산처리를 수행하는 번호인식 알고리즘을 초기화 즉, 현재 프레임의 전체 영역이 아닌 차량영역들로 번호인식 알고리즘을 초기화하는 것에 의해 멀티코어기반의 병렬 처리를 수행할 수 있도록 한다. Meanwhile, the number recognition algorithm for performing the number recognition operation process on the vehicle areas extracted from the current frame is initialized so that the above-described number recognition operation process is parallelly processed corresponding to each extracted vehicle area. By initializing the number recognition algorithm with vehicle areas, it is possible to perform multi-core based parallel processing.
이후, 추출된 차량영역들에 대한 번호 인식 연산 처리가 병렬로 수행될 수 있도록 추출된 차량영역들에 대한 번호인식 연산처리를 수행하여 각 차량 영역으로 초기화된 번호인식 알고리즘은 해당 영역에 대해서만 번호판의 후보 영역 추출, 문자 및 숫자 분리하여 차량번호를 인식하여 출력한다.Thereafter, the number recognition algorithm initialized to each vehicle area by performing the number recognition operation process on the extracted vehicle areas so that the number recognition operation process on the extracted vehicle areas can be performed in parallel is only for the corresponding area. After extracting the candidate area and separating letters and numbers, the vehicle number is recognized and output.
다음으로, 동일성 판단부(114)는 상기 이미지 식별부(113)에서 식별된 차량의 색상, 모양, 전면 번호 및 후면 번호의 동일성을 판단한다.Next, the identity determination unit 114 determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit 113 .
다음으로, 통신부(114)는 상기 차량인식 모듈의 위치정보, 상기 동일성 판단부의 판단결과 및 차량등록부의 등록정보를 후술하는 번호판 위조 대응 서버로 송출한다.Next, the communication unit 114 transmits the location information of the vehicle recognition module, the determination result of the identity determination unit, and the registration information of the vehicle registration unit to a license plate counterfeiting server to be described later.
여기서, 통신부(114)는 차량번호 위조 대응서버와 네트워크로 통신할 수 있고, 상기 네트워크는 RF, 3GPP(3rd Generation Partnership Project) 네트워크, LTE(Long Term Evolution) 네트워크, 5GPP(5rd Generation Partnership Project) 네트워크, WIMAX(World Interoperability for Microwave Access) 네트워크, 인터넷(Internet), LAN(Local Area Network), Wireless LAN(Wireless Local Area Network), WAN(Wide Area Network), PAN(Personal Area Network), 블루투스(Bluetooth) 네트워크, NFC 네트워크, 위성 방송 네트워크, 아날로그 방송 네트워크, DMB(Digital Multimedia Broadcasting) 네트워크 등이 포함되나 이에 한정되지는 않는다.Here, the communication unit 114 may communicate with the vehicle number counterfeit server through a network, and the network may include RF, a 3rd Generation Partnership Project (3GPP) network, a Long Term Evolution (LTE) network, and a 5rd Generation Partnership Project (5GPP) network. , WIMAX (World Interoperability for Microwave Access) network, Internet (Internet), LAN (Local Area Network), Wireless LAN (Wireless Local Area Network), WAN (Wide Area Network), PAN (Personal Area Network), Bluetooth (Bluetooth) A network, an NFC network, a satellite broadcasting network, an analog broadcasting network, a Digital Multimedia Broadcasting (DMB) network, and the like are included, but are not limited thereto.
하기에서, 적어도 하나의 라는 용어는 단수 및 복수를 포함하는 용어로 정의되고, 적어도 하나의 라는 용어가 존재하지 않더라도 각 구성요소가 단수 또는 복수로 존재할 수 있고, 단수 또는 복수를 의미할 수 있음은 자명하다 할 것이다. 또한, 각 구성요소가 단수 또는 복수로 구비되는 것은, 실시예에 따라 변경가능하다할 것이다.In the following, the term at least one is defined as a term including singular and plural, and even if at least one term does not exist, each component may exist in singular or plural, and may mean singular or plural. It will be self-evident. In addition, the singular or plural number of each component may be changed according to embodiments.
다음으로, 도 3에 도시된 바와 같이, 차량번호판 위조 대응 서버(120)는 상기 복수의 차량인식 모듈(110)의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단한 후, 판단결과를 관할기관에 신고하는 구성일 수 있다.Next, as shown in FIG. 3, the license plate counterfeiting server 120 registers the GPS information of the plurality of vehicle recognition modules 110 and the determination result provided by each vehicle recognition module, and the plurality of vehicle recognition modules 110 Based on the determination result provided by the module, if the same vehicle number is detected in different places within a predetermined time, or if the front and rear license plates are different or unregistered license plates, the vehicle is judged for counterfeit license plate and then the determination result is It may be configured to report to the competent authority.
또한, 차량번호판 위조 대응 서버(120)는 기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단한다.In addition, when the license plate forgery response server 120 detects the same license plate number in different regions within a predetermined time, the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B The moving distance and average speed are calculated through the car, and the realistic possibility of movement is determined based on the calculated moving distance and average speed.
또한, 차량번호판 위조 대응 서버(120)는 상기 이동가능성을 판단시에, 기 설정되 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 이동가능성을 판단한다.In addition, when determining the mobility, the license plate counterfeiting server 120 determines the mobility by applying the road traffic volume between area A and area B generated during a preset time period.
보다 구체적으로, 상기 차량번호판 위조 대응 서버(120)는 차량정보 등록부(121), 차량번호 비교판단부(122), 이동거리 및 평균속도 산출부(123), 위조차량 판단부(124) 및 신고부(125)를 포함한다.More specifically, the license plate forgery response server 120 includes a vehicle information registration unit 121, a vehicle number comparison and determination unit 122, a moving distance and average speed calculation unit 123, a counterfeit vehicle quantity determination unit 124 and a report section 125.
상기 차량정보 등록부(121)는 차량인식 모듈(110)에서 송출된 차량인식 장소의 GPS 정보, 판단결과(차번인식 결과)를 시간대별로 등록하는 구성일 수 있다.The vehicle information registration unit 121 may be configured to register the GPS information of the vehicle recognition location transmitted from the vehicle recognition module 110 and the determination result (vehicle identification result) by time slot.
다음으로, 차량번호 비교판단부(122)는 서로 다른 장소에 위치하는 차량번호 인식모듈에서 전송된 차량번호의 동일성 여부를 판단한다.Next, the vehicle number comparison and determination unit 122 determines whether the vehicle numbers transmitted from the vehicle number recognition modules located in different places are identical.
다음으로, 상기 이동거리 및 평균속도 산출부(123)는 기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출한다.Next, when the same vehicle number is detected in different regions within a predetermined time, the moving distance and average speed calculation unit 123 calculates the vehicle number recognition time of the vehicle recognition module in region A and the vehicle recognition module in region B. The moving distance and average speed are calculated through the difference between vehicle number recognition times.
다음으로, 위조차량 판단부(124)는 상기 이동거리 및 평균속도 산출부(123)에서 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단한다. 여기서, 현실적 이동거리는 물리적으로 기 설정된 시간 내에 이동가능성을 의미한다.Next, the counterfeit distance determination unit 124 determines the actual possibility of movement based on the moving distance and average speed calculated by the moving distance and average speed calculating unit 123 . Here, the realistic movement distance means the possibility of movement within a physically preset time.
또한, 위조차량 판단부(124)는 외부서버로부터 기 설정된 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 현실적 이동가능성을 판단할 수 있다.In addition, the counterfeit vehicle weight determination unit 124 may determine realistic mobility by applying the road traffic volume between regions A and B generated during a preset time from an external server.
상기 신고부(125)는 위조차량 판단부(124)에서 위조차량으로 판단된 차량의 차량번호 및 차량위조로 인식된 장소의 GPS 정보를 관할기관으로 전송한다.The report unit 125 transmits the license plate number of the vehicle determined to be a counterfeit vehicle in the counterfeit vehicle determination unit 124 and the GPS information of the place recognized as a counterfeit vehicle to the competent authority.
도 4는 본 발명의 일 실시예에 따른 차량번호판 위조 대응 방법을 설명한 흐름도이다.4 is a flowchart illustrating a method for coping with license plate counterfeiting according to an embodiment of the present invention.
도 4를 참조하면, 본 발명의 일 실시예에 따른 차량번호판 위조 대응 방법(S700)은 서로 다른 장소에 설치된 복수의 차량인식 모듈 각각에서 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공(S710)하면, 차량번호판 위조 대응 서버(120)에서 상기 복수의 차량인식 모듈의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단(S720)한다.Referring to FIG. 4 , in the vehicle license plate forgery counterfeiting method (S700) according to an embodiment of the present invention, image information of front and rear license plates of a vehicle photographed by a camera unit is obtained from each of a plurality of vehicle recognition modules installed in different places. After inputting, after recognizing the character strings and numbers in the front and rear license plates, if the determination result of determining whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and whether they are identical is provided (S710), the vehicle license plate The forgery response server 120 registers the GPS information of the plurality of vehicle recognition modules and the determination result provided from each vehicle recognition module, and at different places within a predetermined time based on the determination result provided from the plurality of vehicle recognition modules. When the same vehicle number is detected, or the front and rear license plates are different, or unregistered license plates are detected, the vehicle is judged to be a counterfeit license plate (S720).
이후, 차량번호판 대응 서버(120)에서 판단결과를 관할기관에 신고(S730)한다.Thereafter, the license plate corresponding server 120 reports the judgment result to the competent authority (S730).
여기서, 상기 S720 과정은 기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단하는 과정일 수 있다.Here, in the step S720, when the same license plate number is detected in different regions within a predetermined time, the moving distance through the difference between the vehicle number recognition time of the vehicle recognition module in region A and the vehicle number recognition time recognized by the vehicle recognition module in region B and a process of calculating an average speed and determining a realistic possibility of movement based on the calculated moving distance and average speed.
또한, 상기 S720 과정은 상기 이동가능성을 판단시에, 기 설정되 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 이동가능성을 판단할 수 있다.In addition, in step S720, when determining the mobility, the possibility of mobility may be determined by applying the road traffic volume between area A and area B generated during a preset time period.
따라서, 본 발명의 일 실시예에 따른 차량 번호판 위조 대응 시스템 및 방법을 이용하면, 종래의 차량번호 위조차량에 대해서 담당자가 육안으로 차량번호를 육안으로 확인하던 방식으로 자동으로 위조 여부를 판단하게 함으로서, 위조 차량의 적발성에 대한 정확성 및 이에 소요되는 시간을 최소화시킬 수 있다는 이점이 있다.Therefore, using the vehicle license plate forgery countermeasure system and method according to an embodiment of the present invention, for conventional vehicle number counterfeit vehicles, the person in charge automatically determines whether the vehicle number is forged by visually checking the vehicle number with the naked eye. However, there is an advantage in that the accuracy of detecting counterfeit vehicles and the time required for this can be minimized.
상술한 이점을 통해 교통위법 단속 주기관과 실시간 연동함으로써 전국 각지에 불법으로 시행되는 차량번호 위조 차량을 동시다발적으로 적발할 수 있다는 이점이 있다.Through the above-mentioned advantages, there is an advantage in that it is possible to simultaneously detect counterfeit license plate vehicles illegally implemented in various parts of the country by linking with the traffic violation control agency in real time.
도 5는 본 명세서에 개진된 하나 이상의 실시예가 구현될 수 있는 예시적인 컴퓨팅 환경을 도시하는 도면으로, 상술한 하나 이상의 실시예를 구현하도록 구성된 컴퓨팅 디바이스(1100)를 포함하는 시스템(1000)의 예시를 도시한다. 예를 들어, 컴퓨팅 디바이스(1100)는 개인 컴퓨터, 서버 컴퓨터, 핸드헬드 또는 랩탑 디바이스, 모바일 디바이스(모바일폰, PDA, 미디어 플레이어 등), 멀티프로세서 시스템, 소비자 전자기기, 미니 컴퓨터, 메인프레임 컴퓨터, 임의의 전술된 시스템 또는 디바이스를 포함하는 분산 컴퓨팅 환경 등을 포함하지만, 이것으로 한정되는 것은 아니다.5 is a diagram illustrating an example computing environment in which one or more embodiments set forth herein may be implemented, an illustration of a system 1000 that includes a computing device 1100 configured to implement one or more embodiments described above. shows For example, computing device 1100 may be a personal computer, server computer, handheld or laptop device, mobile device (mobile phone, personal digital assistant, media player, etc.), multiprocessor system, consumer electronics, mini computer, mainframe computer, distributed computing environments that include any of the foregoing systems or devices; and the like.
컴퓨팅 디바이스(1100)는 적어도 하나의 프로세싱 유닛(1110) 및 메모리(1120)를 포함할 수 있다. 여기서, 프로세싱 유닛(1110)은 예를 들어 중앙처리장치(CPU), 그래픽처리장치(GPU), 마이크로프로세서, 주문형 반도체(application Specific Integrated Circuit, ASIC), Field Programmable Gate Arrays(FPGA) 등을 포함할 수 있으며, 복수의 코어를 가질 수 있다. 메모리(1120)는 휘발성 메모리(예를 들어, RAM 등), 비휘발성 메모리(예를 들어, ROM, 플래시 메모리 등) 또는 이들의 조합일 수 있다. 또한, 컴퓨팅 디바이스(1100)는 추가적인 스토리지(1130)를 포함할 수 있다. 스토리지(1130)는 자기 스토리지, 광학 스토리지 등을 포함하지만 이것으로 한정되지 않는다. 스토리지(1130)에는 본 명세서에 개진된 하나 이상의 실시예를 구현하기 위한 컴퓨터 판독 가능한 명령이 저장될 수 있고, 운영 시스템, 애플리케이션 프로그램 등을 구현하기 위한 다른 컴퓨터 판독 가능한 명령도 저장될 수 있다. 스토리지(1130)에 저장된 컴퓨터 판독 가능한 명령은 프로세싱 유닛(1110)에 의해 실행되기 위해 메모리(1120)에 로딩될 수 있다. 또한, 컴퓨팅 디바이스(1100)는 입력 디바이스(들)(1140) 및 출력 디바이스(들)(1150)을 포함할 수 있다. Computing device 1100 may include at least one processing unit 1110 and memory 1120 . Here, the processing unit 1110 may include, for example, a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), and the like. and may have a plurality of cores. The memory 1120 may be volatile memory (eg, RAM, etc.), non-volatile memory (eg, ROM, flash memory, etc.), or a combination thereof. Additionally, computing device 1100 may include additional storage 1130 . Storage 1130 includes, but is not limited to, magnetic storage, optical storage, and the like. The storage 1130 may store computer readable instructions for implementing one or more embodiments disclosed herein, and may also store other computer readable instructions for implementing an operating system, application programs, and the like. Computer readable instructions stored in storage 1130 may be loaded into memory 1120 for execution by processing unit 1110 . Computing device 1100 can also include input device(s) 1140 and output device(s) 1150 .
여기서, 입력 디바이스(들)(1140)은 예를 들어 키보드, 마우스, 펜, 음성 입력 디바이스, 터치 입력 디바이스, 적외선 카메라, 비디오 입력 디바이스 또는 임의의 다른 입력 디바이스 등을 포함할 수 있다. 또한, 출력 디바이스(들)(1150)은 예를 들어 하나 이상의 디스플레이, 스피커, 프린터 또는 임의의 다른 출력 디바이스 등을 포함할 수 있다. 또한, 컴퓨팅 디바이스(1100)는 다른 컴퓨팅 디바이스에 구비된 입력 디바이스 또는 출력 디바이스를 입력 디바이스(들)(1140) 또는 출력 디바이스(들)(1150)로서 사용할 수도 있다.Here, input device(s) 1140 may include, for example, a keyboard, mouse, pen, voice input device, touch input device, infrared camera, video input device, or any other input device. Output device(s) 1150 may also include, for example, one or more displays, speakers, printers, or any other output devices, or the like. Additionally, computing device 1100 may use an input device or output device included in another computing device as input device(s) 1140 or output device(s) 1150 .
또한, 컴퓨팅 디바이스(1100)는 컴퓨팅 디바이스(1100)가 다른 디바이스(예를 들어, 컴퓨팅 디바이스(1300))와 통신할 수 있게 하는 통신접속(들)(1160)을 포함할 수 있다. Computing device 1100 may also include communication connection(s) 1160 that allow computing device 1100 to communicate with other devices (eg, computing device 1300).
여기서, 통신 접속(들)(1160)은 모뎀, 네트워크 인터페이스 카드(NIC), 통합 네트워크 인터페이스, 무선 주파수 송신기/수신기, 적외선 포트, USB 접속 또는 컴퓨팅 디바이스(1100)를 다른 컴퓨팅 디바이스에 접속시키기 위한 다른 인터페이스를 포함할 수 있다. 또한, 통신 접속(들)(1160)은 유선 접속 또는 무선 접속을 포함할 수 있다. 상술한 컴퓨팅 디바이스(1100)의 각 구성요소는 버스 등의 다양한 상호접속(예를 들어, 주변 구성요소 상호접속(PCI), USB, 펌웨어(IEEE 1394), 광학적 버스 구조 등)에 의해 접속될 수도 있고, 네트워크(1200)에 의해 상호접속될 수도 있다. 본 명세서에서 사용되는 "구성요소", "시스템" 등과 같은 용어들은 일반적으로 하드웨어, 하드웨어와 소프트웨어의 조합, 소프트웨어, 또는 실행중인 소프트웨어인 컴퓨터 관련 엔티티를 지칭하는 것이다. Here, communication connection(s) 1160 may be a modem, network interface card (NIC), integrated network interface, radio frequency transmitter/receiver, infrared port, USB connection, or other device for connecting computing device 1100 to other computing devices. May contain interfaces. Further, communication connection(s) 1160 may include a wired connection or a wireless connection. Each component of the aforementioned computing device 1100 may be connected by various interconnections such as a bus (eg, peripheral component interconnection (PCI), USB, firmware (IEEE 1394), optical bus structure, etc.) and may be interconnected by the network 1200. Terms such as "component" and "system" as used herein generally refer to a computer-related entity that is hardware, a combination of hardware and software, software, or software in execution.
예를 들어, 구성요소는 프로세서 상에서 실행중인 프로세스, 프로세서, 객체, 실행 가능물(executable), 실행 스레드, 프로그램 및/또는 컴퓨터일 수 있지만, 이것으로 한정되는 것은 아니다. 예를 들어, 컨트롤러 상에서 구동중인 애플리케이션 및 컨트롤러 모두가 구성요소일 수 있다. 하나 이상의 구성요소는 프로세스 및/또는 실행의 스레드 내에 존재할 수 있으며, 구성요소는 하나의 컴퓨터 상에서 로컬화될 수 있고, 둘 이상의 컴퓨터 사이에서 분산될 수도 있다.For example, a component may be, but is not limited to, a process running on a processor, a processor, an object, an executable, a thread of execution, a program, and/or a computer. For example, both the application running on the controller and the controller may be components. One or more components can reside within a process and/or thread of execution and a component can be localized on one computer or distributed between two or more computers.
본 발명이 속하는 기술분야의 당업자는 본 발명이 그 기술적 사상이나 필수적 특징을 변경하지 않고서 다른 구체적인 형태로 실시될 수 있다는 것을 이해할 수 있을 것이다. 그러므로 이상에서 기술한 실시예는 모든 면에서 예시적인 것이며 한정적인 것이 아닌 것으로서 이해되어야 하고, 본 발명의 범위는 상기 상세한 설명보다는 후술하는 특허청구범위에 의하여 나타내어지며, 특허청구범위의 의미 및 범위 그리고 그 등가개념으로부터 도출되는 모든 변경 또는 변형된 형태가 본 발명의 범위에 포함되는 것으로 해석되어야 한다.Those skilled in the art to which the present invention pertains will understand that the present invention may be embodied in other specific forms without changing its technical spirit or essential features. Therefore, the embodiments described above should be understood as illustrative and not restrictive in all respects, and the scope of the present invention is indicated by the claims to be described later rather than the detailed description, and the meaning and scope of the claims and All changes or modified forms derived from the equivalent concept should be construed as being included in the scope of the present invention.
*부호의 설명**Description of code*
100: 차량 번호판 위조 대응 시스템100: Vehicle license plate forgery response system
110: 차량인식 모듈110: vehicle recognition module
111: 차량등록부111: vehicle registration
112: 카메라부112: camera unit
113: 이미지 식별부113: image identification unit
114: 동일성 판단부114: identity determination unit
115: 통신부115: communication department
120: 차량번호판 위조 대응 서버120: license plate forgery response server
121: 차량정보 등록부121: vehicle information register
122: 차량번호 비교판단부122: vehicle number comparison judgment unit
123: 이동거리 및 평균속도 산출부123: moving distance and average speed calculation unit
124: 위조차량 판단부124: counterfeit weight determination unit
125: 신고부125: report

Claims (7)

  1. 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공하는 서로 다른 장소에 설치된 복수의 차량인식 모듈; 및After inputting the image information of the front and rear license plates of the vehicle photographed by the camera unit, after recognizing the character strings and numbers in the front and rear license plates, whether the letters and numbers of the front license plate and the letters and numbers of the rear license plate are registered and A plurality of vehicle recognition modules installed in different places to provide a determination result of determining whether or not they are identical; and
    상기 복수의 차량인식 모듈의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단한 후, 판단결과를 관할기관에 신고하는 번호판 위조 대응 서버를 포함하는 차량 번호판 위조 대응 시스템.The GPS information of the plurality of vehicle recognition modules and the determination result provided by each vehicle recognition module are registered, and the same vehicle number is detected in different places within a predetermined time based on the determination result provided by the plurality of vehicle recognition modules, or Vehicle license plate forgery countermeasure system including a license plate forgery response server that judges the amount of license plate forgery of the vehicle and reports the judgment result to the competent authority if the front and rear license plates are different or unregistered license plates.
  2. 제1항에 있어서,According to claim 1,
    상기 복수의 차량인식 모듈은The plurality of vehicle recognition modules
    차량번호, 색상, 차종, 연식을 등록한 차량등록부;A vehicle registration book that registers vehicle number, color, model, and year;
    차량의 전면 및 후면을 촬영하는 카메라부;A camera unit for taking pictures of the front and rear of the vehicle;
    상기 카메라부에서 촬영된 전면 이미지 및 후면 이미지 내의 차량색상, 차량모양, 숫자 및 문자열을 식별하는 이미지 식별부;an image identification unit for identifying vehicle color, vehicle shape, number and character string in the front image and rear image captured by the camera unit;
    상기 이미지 식별부에서 식별된 차량의 색상, 모양, 전면 번호 및 후면 번호의 동일성을 판단하는 동일성 판단부; 및an identity determination unit that determines the identity of the color, shape, front number and rear number of the vehicle identified by the image identification unit; and
    상기 차량인식 모듈의 위치정보, 상기 동일성 판단부의 판단결과 및 차량등록부의 등록정보를 상기 번호판 위조 대응 서버로 송출하는 통신부를 포함하는 차량 번호판 위조 대응 시스템.and a communication unit configured to transmit location information of the vehicle recognition module, a determination result of the identity determination unit, and registration information of the vehicle registration unit to the number plate counterfeiting response server.
  3. 제1항에 있어서,According to claim 1,
    상기 번호판 위조 대응서버는The license plate counterfeiting server
    기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단하는 것을 특징으로 하는 차량 번호판 위조 대응 시스템.If the same license plate number is detected in different regions within a preset time, the moving distance and average speed are calculated through the difference between the vehicle number recognition time of the vehicle recognition module in region A and the number recognition time recognized by the vehicle recognition module in region B. , Vehicle license plate forgery response system, characterized in that for determining the realistic mobility based on the calculated moving distance and average speed.
  4. 제3항에 있어서,According to claim 3,
    상기 번호판 위조 대응서버는The license plate counterfeiting server
    상기 이동가능성을 판단시에, 기 설정되 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 이동가능성을 판단하는 것을 특징으로 하는 차량 번호판 위조 대응 시스템.When determining the mobility, the vehicle license plate forgery response system, characterized in that for determining the mobility by applying the road traffic volume between area A and area B generated during a predetermined time.
  5. 서로 다른 장소에 설치된 복수의 차량인식 모듈 각각에서 카메라부에서 촬영된 차량의 앞, 뒤 번호판의 이미지 정보를 입력한 후, 상기 앞, 뒤 번호판 내의 문자열 및 숫자를 인식한 후, 앞 번호판의 문자 및 숫자와 뒤 번호판의 문자 및 숫자의 등록여부 및 동일성 여부를 판단한 판단결과를 제공하는 단계; 및In each of a plurality of vehicle recognition modules installed in different places, after inputting the image information of the front and rear license plates of the vehicle photographed by the camera unit, after recognizing the character strings and numbers in the front and rear license plates, the letters and numbers of the front license plates Providing a determination result of determining whether the numbers and letters and numbers of the rear license plate are registered and identical; and
    번호판 위조 대응 서버에서 상기 복수의 차량인식 모듈의 GPS 정보 및 각 차량인식 모듈에서 제공된 판단결과를 등록하고, 상기 복수의 차량인식 모듈에서 제공된 판단결과를 기초로 기 설정된 시간 내에 서로 다른 장소에서 동일한 차량번호가 감지되거나 또는 앞, 뒤 번호판의 상이하거나 또는 미등록된 번호판일 경우, 해당 차량을 번호판 위조차량을 판단하는 단계; 및The license plate forgery response server registers the GPS information of the plurality of vehicle recognition modules and the determination result provided from each vehicle recognition module, and the same vehicle in different places within a predetermined time based on the determination result provided from the plurality of vehicle recognition modules. If the number is detected, the front and rear license plates are different, or the license plate is unregistered, determining whether the vehicle is falsified license plate; and
    판단결과를 관할기관에 신고하는 단계를 포함하는 차량 번호판 위조 대응 방법.A method for counterfeiting license plate counterfeiting, including reporting the judgment result to the competent authority.
  6. 제5항에 있어서,According to claim 5,
    상기 판단하는 단계는The judging step is
    기 설정된 시간 내에 서로 다른 지역에서 동일한 차량번호가 감지되면, A 지역의 차량인식 모듈의 차번인식 시간과 B 지역의 차량인식 모듈에서 인식한 차번인식 시간 간의 차를 통해 이동거리 및 평균속도를 산출하고, 산출된 이동거리 및 평균속도를 기초로 현실적 이동가능성을 판단하는 단계인 것을 특징으로 하는 차량 번호판 위조 대응 방법.If the same license plate number is detected in different regions within a preset time, the moving distance and average speed are calculated through the difference between the vehicle number recognition time of the vehicle recognition module in region A and the number recognition time recognized by the vehicle recognition module in region B. , Vehicle license plate forgery countermeasure method, characterized in that the step of determining the realistic mobility based on the calculated moving distance and average speed.
  7. 제6항에 있어서,According to claim 6,
    상기 판단하는 단계는The judging step is
    상기 이동가능성을 판단시에, 기 설정되 시간 동안 발생된 A 지역과 B 지역 간의 도로교통량을 적용하여 이동가능성을 판단하는 단계를 더 포함하는 것을 특징으로 하는 차량 번호판 위조 대응 방법.When determining the mobility, the step of determining the mobility by applying the road traffic volume between area A and area B generated during a predetermined time period to determine the possibility of movement.
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