CN113470206A - Highway inspection method, device and medium based on vehicle matching - Google Patents
Highway inspection method, device and medium based on vehicle matching Download PDFInfo
- Publication number
- CN113470206A CN113470206A CN202110750681.3A CN202110750681A CN113470206A CN 113470206 A CN113470206 A CN 113470206A CN 202110750681 A CN202110750681 A CN 202110750681A CN 113470206 A CN113470206 A CN 113470206A
- Authority
- CN
- China
- Prior art keywords
- vehicle
- determining
- information
- arrival
- arrival time
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G07—CHECKING-DEVICES
- G07B—TICKET-ISSUING APPARATUS; FARE-REGISTERING APPARATUS; FRANKING APPARATUS
- G07B15/00—Arrangements or apparatus for collecting fares, tolls or entrance fees at one or more control points
- G07B15/06—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems
- G07B15/063—Arrangements for road pricing or congestion charging of vehicles or vehicle users, e.g. automatic toll systems using wireless information transmission between the vehicle and a fixed station
-
- 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
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Business, Economics & Management (AREA)
- Finance (AREA)
- Devices For Checking Fares Or Tickets At Control Points (AREA)
- Traffic Control Systems (AREA)
Abstract
The application discloses a vehicle matching-based highway inspection method, equipment and medium, wherein the method comprises the following steps: determining a vehicle structural model of the ETC system; identifying the vehicle of the ETC lane through a vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time; searching in the artificial charging system according to the characteristic information, and determining that the artificial charging system comprises the characteristic information and second station entering time; and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information. According to the embodiment of the application, the station entering information corresponding to the latest station entering time is determined as the station entering information of the vehicle by comparing the station entering time of the ETC system with the station entering time of the manual toll collection system, the effective station entering information can be automatically generated, and the phenomenon that the ETC card and the cash card mutually fall away for fee can be avoided by taking the last station entering information as the standard.
Description
Technical Field
The application relates to the technical field of intelligent traffic, in particular to a method, equipment and medium for checking an expressway based on vehicle matching.
Background
Intelligent transportation can provide diversified services for transportation participants, and is a development direction of future transportation systems, and ETC (Electronic Toll Collection) systems are widely applied under the trend.
At present, in an ETC system, microwave communication is performed between an RSU (Road Side Unit) and an OBU on a vehicle to obtain license plates and vehicle type characteristics carried in information fed back by the OBU, so that intelligent charging without stopping the vehicle is realized.
However, under a new charging system, the highway charging auditing service also gradually faces some difficulties, such as missing toll, difficulty in additional payment, and the like, resulting in low management efficiency.
Disclosure of Invention
The embodiment of the application provides a vehicle matching-based highway inspection method, device and medium, which are used for solving the problem of low management efficiency of highway toll audit business in an ETC system.
The embodiment of the application adopts the following technical scheme:
in one aspect, an embodiment of the present application provides a highway inspection method based on vehicle matching, where the method includes: determining a vehicle structural model of the ETC system; identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time; searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time; and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
In one example, the identifying the vehicle in the ETC lane through the vehicle structural model and determining the characteristic information of the vehicle specifically include: identifying the vehicle of the ETC lane through the vehicle structural model, and determining a detection area of the vehicle; calculating the gray channels in the neighborhood of the pixels in the detection area and the gray channel of the background picture to determine a normalized correlation coefficient; if the difference value between the numerical value of the normalized correlation coefficient and 1 is smaller than a preset threshold value, determining that the pixel is a shadow; and eliminating the shadow to determine the vehicle type information of the vehicle.
In one example, if the difference between the value of the normalized correlation coefficient and 1 is smaller than a preset threshold, after the pixel is determined to be a shadow, the method further includes: inputting the pixel into a Gaussian mixture model; and if the pixel is successfully matched with the Gaussian distribution of the Gaussian mixture model, finishing the verification that the pixel is a shadow.
In one example, determining the gaussian mixture model specifically includes: obtaining a sample detection area; establishing an initial Gaussian mixture model for each pixel in the sample detection area; respectively updating the initial Gaussian mixture model according to the value of each pixel marked as the sample detection area to obtain a plurality of updated Gaussian mixture models; determining an updated Gaussian mixture model corresponding to the Gaussian distribution with the maximum weight in the plurality of updated Gaussian mixture models; determining the updated Gaussian mixture model as the Gaussian mixture model.
In one example, the identifying the vehicle in the ETC lane through the vehicle structural model and determining the characteristic information of the vehicle specifically include: identifying the vehicle of the ETC lane through the vehicle structural model, and determining the license plate position of the vehicle; selecting an area above the license plate position according to the license plate position; the upper region comprises a region not greater than a preset distance threshold; and determining the body color of the vehicle according to the area.
In one example, the determining the color of the vehicle according to the area specifically includes: determining that the vehicle does not include a colored vehicle; dividing the regions into N parts equally, and calculating the variance of each region in an S channel to obtain the S variance of each region; determining an area corresponding to the S variance which is not greater than a preset variance threshold as an identification area; carrying out color identification on the identification area, and determining the color of the identification area; and voting the colors of the identification areas to determine the body color of the vehicle.
In one example, after comparing the first arrival time with the second arrival time and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle, the method further includes: determining the vehicles with the same license plate corresponding to the vehicles within a preset time period; the vehicles with the same license plate number are the same as the license plate number of the vehicle; comparing the characteristic information of the vehicle with the same license plate to obtain a comparison result of each characteristic information; sorting the comparison results through the similarity, and determining a comparison result queue; and comparing the running speeds and the space-time relationship of the vehicles and the vehicles with the same license plate according to the comparison result queue so as to identify the vehicles with the same license plate number in running on different routes.
In one example, after comparing the first arrival time with the second arrival time and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle, the method further includes: retrieving the vehicle in a vehicle archive according to the characteristic information; if the vehicle exists in the vehicle archive, determining historical characteristic information of the vehicle in the vehicle archive; comparing the feature information with the historical feature information; and if the vehicle is inconsistent, determining the vehicle as a suspect large-vehicle small-mark vehicle, and giving an alarm in the ETC system.
In one example, the comparing the running speeds and the time-space relationship between the vehicle and the vehicle with the same license plate according to the comparison result queue to identify the vehicles with the same license plate number in running on different routes specifically includes: if the average value of the similarity values of the comparison result queue is not smaller than a preset similarity threshold value; the difference value of the running speeds is not less than a preset difference value threshold value, and the time-space relationship between the vehicle and the vehicle with the same license plate is different; and determining the vehicle and the vehicle with the same license plate as the vehicles with the same license plate number running on different routes.
In another aspect, an embodiment of the present application provides a highway checking device based on vehicle matching, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to: determining a vehicle structural model of the ETC system; identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time; searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time; and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
In another aspect, an embodiment of the present application provides a non-volatile computer storage medium for highway inspection based on vehicle matching, which stores computer-executable instructions, and is characterized in that the computer-executable instructions are configured to: determining a vehicle structural model of the ETC system; identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time; searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time; and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
The embodiment of the application adopts at least one technical scheme which can achieve the following beneficial effects:
according to the embodiment of the application, the station entering information corresponding to the latest station entering time is determined to be the station entering information of the vehicle by comparing the station entering time of the ETC system with the station entering time of the manual toll collection system, the vehicle is charged according to the station entering information, one-time effective station entering information can be automatically allowed to be generated, and the last station entering information is taken as the standard, so that the phenomenon that the ETC card and the cash card escape from each other can be avoided, and the management efficiency of highway inspection is improved.
Drawings
In order to more clearly explain the technical solutions of the present application, some embodiments of the present application will be described in detail below with reference to the accompanying drawings, in which:
fig. 1 is a schematic flowchart of a highway inspection method based on vehicle matching according to an embodiment of the present disclosure;
fig. 2 is a schematic structural diagram of an expressway inspection device based on vehicle matching according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the technical solutions of the present application will be described in detail and completely with reference to the following embodiments and accompanying drawings. It should be apparent that the described embodiments are only some of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The analysis method according to the embodiment of the present application may be implemented by a terminal device or a server, and the present application is not limited to this. For convenience of understanding and description, the following embodiments are described in detail by taking a terminal device as an example.
It should be noted that the server may be a single device, or may be a system composed of multiple devices, that is, a distributed server, which is not specifically limited in this application.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flowchart of an expressway inspection method based on vehicle matching according to an embodiment of the present application, which specifically includes the following steps:
s101: and determining a vehicle structural model of the ETC system.
In some implementations of the present application, a vehicle structural model is deployed at each edge end for real-time analysis of vehicles traveling on the highway, and a server builds a vehicle archive and stores the vehicles initially entering the highway to the vehicle archive.
The edge end comprises each monitoring end for identifying vehicles through an ETC system, such as a toll station, a service area, a microwave vehicle inspection area, a confluence area, a ramp entrance and exit, a road section high-definition camera and the like.
It should be noted that the vehicle structural model may be an existing vehicle feature recognition algorithm, and is not limited in detail herein.
S102: the method comprises the steps of identifying vehicles of an ETC lane through a vehicle structural model, and determining characteristic information of the vehicles and a first arrival time.
In some implementations of the application, after a vehicle enters an ETC lane, a head image snapshot unit of the multi-dimensional vehicle characteristic recognition system automatically takes a snapshot of a head photo, a vehicle side image snapshot unit scans a vehicle side image in real time, the number of vehicle side wheels and the vehicle side length are judged through a vehicle type recognition matching algorithm to distinguish vehicle types, a vehicle tail image snapshot unit takes a snapshot of a vehicle tail image, after the vehicle passes through, vector matching and time matching are carried out through a queue algorithm, three groups of snapshot images are recognized, and characteristic information of the vehicle is obtained. The characteristic information of the vehicle comprises a license plate number, a license plate position, a license plate color, a vehicle type, a vehicle body color and the like. For example, the color of the license plate includes a yellow license plate and a blue license plate, the vehicle type includes a small vehicle, a large vehicle and the like, and the color of the vehicle body includes a color vehicle and a black-white gray vehicle.
Of course, when the vehicle enters the ETC lane, the ETC system also records the arrival time (referred to as the first arrival time) of the vehicle, the arrival time and the characteristic information are stored in a corresponding archive, and the first arrival time can be retrieved from the archive through the characteristic information.
S103: and searching in the manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and the second station entering time.
In some implementations of the present application, there is a phenomenon in which the ETC card and the cash card mutually evade fees. For example, if a vehicle equipped with OBU equipment is successfully swiped from an A station ETC lane, the vehicle is intentionally poured out, a cash card is normally taken from an artificial lane to drive in, and a cash card is normally paid for driving out at an outlet. Get cash card from B station and drive in during the journey returns, utilize the ETC card in the information of staying to drive out at the C station that is close apart from A station high speed during the export, reached run long buy short purpose through the ETC after the station that enters, then withdraw from to get IC card at artifical charging passageway, thereby flee for the fee.
Therefore, the ETC system is networked with the manual toll collection system, and then the server searches whether the characteristic information of the vehicle is included in the manual toll collection system after obtaining the characteristic information of the vehicle, and if the characteristic information of the vehicle is not included, the information of the vehicle entering the station, which is currently recorded in the ETC system, is used as the information of the vehicle entering the station on the expressway.
Of course, when the vehicle enters the station via the manual toll lane, the manual toll system also records the station entering time (referred to as the second station entering time), the station entering time and the characteristic information are stored in the corresponding archive, and the second station entering time can be retrieved from the archive through the characteristic information.
S104: and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
In some implementations of the present application, when the current vehicle travels on a highway, since the manual toll collection system and the ETC system have the arrival time information of the vehicle at the same time, the server needs to compare the arrival time of the vehicle in the ETC system and the manual toll collection system, and use the latest arrival time as the arrival time of the vehicle, so as to charge the vehicle according to the arrival information. Namely, the vehicle takes the last arrival information as the standard, and the vehicle automatically prompts to delete the previous arrival information.
It should be noted that, although the embodiment of the present application describes steps S101 to S104 in sequence with reference to fig. 1, this does not mean that steps S101 to S104 must be executed in strict sequence. The embodiment of the present application is described by sequentially describing step S101 to step S104 according to the sequence shown in fig. 1, so as to facilitate those skilled in the art to understand the technical solutions of the embodiment of the present application. In other words, in the embodiment of the present application, the sequence between step S101 and step S104 may be appropriately adjusted according to actual needs.
Through the method of fig. 1, in the embodiment of the application, the station entry information corresponding to the latest station entry time is determined as the station entry information of the vehicle by comparing the station entry times of the ETC system and the manual toll collection system, so that the vehicle is charged according to the station entry information, one-time effective station entry information can be automatically allowed to be generated, and the last station entry information is taken as the standard, thereby avoiding the phenomenon that the ETC card and the cash card mutually fall away from each other, and improving the management efficiency of the highway inspection.
Based on the method of fig. 1, the examples of the present application also provide some specific embodiments and extensions of the method, and the following description is continued.
In some implementations of the present application, when determining the vehicle type of the vehicle, if the vehicle type of the vehicle is determined directly through the detection area identified by the vehicle structural model, complexity of the image scene may be ignored, so that the length estimation of the vehicle may be inaccurate. That is, due to the complexity of the image scene, shadows may be included in the detection area.
The target picture includes a background area and a detection area, and the detection area is an area occupied by a vehicle contour.
Therefore, after the server identifies the vehicle of the ETC lane through the vehicle structural model and determines the detection area of the vehicle, the server optimizes the detection area, namely eliminates the shadow of the detection area.
Further, on the gray scale image of the target picture, the gray scale values of the pixel points are in a linear relationship when the pixel points are covered by the shadow and not covered by the shadow, that is, the gray scale values of the input shadow pixel and the background pixel are in a linear relationship, and the ratio of the shadow gray scale value to the background gray scale value is basically kept constant for different pixels, so that the correlation coefficient of the shadow area and the background area is close to 1.
Therefore, the server calculates the normalized correlation coefficient between the gray channel in the neighborhood of the pixel in the detection area and the gray channel of the background picture, and if the difference value between the numerical value of the correlation coefficient and 1 is smaller than a preset threshold value, the pixel is judged to be a shadow, so that the shadow is eliminated, and the vehicle type information of the vehicle is obtained. That is, if the calculated correlation coefficient value is close to 1, the pixel is determined as a shadow.
According to the embodiment of the application, the shadow area in the detection area is eliminated, the detection area can be optimized, and the identification accuracy of the vehicle type is improved.
In some implementations of the present application, the determination of the shadow may need to be verified, as the determination of the shadow may not be accurate enough.
Specifically, since the color of the shade is relatively stable over a certain period of time, the change with time is small, but the true vehicle color changes greatly. That is, the shadows have consistency in the time dimension. For a certain pixel, the distribution of values on different channels when the pixel is marked as a running pixel is counted, and the value with the largest occurrence frequency corresponds to a shadow.
Therefore, the server pre-constructs a Gaussian mixture model, inputs the pixels judged as the shadows into the Gaussian mixture model, and when the pixels are matched with the Gaussian distribution of the Gaussian mixture model, if the matching is successful, the shadows are eliminated through verification to obtain the vehicle type information of the vehicle.
When a Gaussian mixture model is constructed, a large number of vehicle side snapshot pictures of various vehicle types are obtained to serve as samples, a sample detection area is obtained, an initial Gaussian mixture model is established for each pixel in the sample detection area, then the respective Gaussian mixture model is updated by adopting the value when the current pixel is marked as a motion area, a plurality of updated Gaussian mixture models are obtained, and then the updated Gaussian mixture model corresponding to the Gaussian distribution with the maximum weight is determined to be the Gaussian mixture model in the updated Gaussian mixture models. That is, the gaussian distribution corresponding to the shadow will have the greatest weight after a period of learning.
According to the embodiment of the application, the mixed Gaussian model is constructed in advance, the judgment of the shadow needs to be verified, and the identification accuracy rate of the vehicle type can be further improved.
In some implementations of the present application, when determining the color of the vehicle body, the accuracy of the color recognition of the vehicle body mainly depends on the recognition area of the color of the vehicle body, but when performing the area segmentation, there may be some interferences, such as a hanging object, a window, a heat sink in front of the vehicle, and the like, and it cannot be guaranteed that all the selected areas are the color area of the vehicle body.
Therefore, the server identifies the picture of the head of the vehicle through the vehicle structural model, and after the license plate position of the vehicle is determined, the color of the vehicle body is obtained by combining the license plate position.
Specifically, the server selects an area above the license plate position. The upper area is an area which is not greater than a preset distance threshold. And judging whether the selected area is colored or not, namely judging whether the current vehicle is a colored vehicle or not, and if so, identifying the color of the colored area through a color identification algorithm to obtain the color of the vehicle body. If the vehicle is not a color vehicle, namely a black-white gray vehicle, dividing the regions into N parts equally, calculating the variance of each region in an S channel, selecting a plurality of regions corresponding to the S variance which is not more than a preset variance threshold value as identification regions, then carrying out color identification on the identification regions to obtain the colors of the identification regions, and finally voting the colors of the identification regions to determine the vehicle body color of the vehicle.
According to the embodiment of the application, the color of the vehicle body is obtained by combining the position of the license plate, the influence of interference objects can be reduced, and the accuracy rate of recognizing the color of the vehicle body is improved.
In some implementations of the present application, in order to find out a vehicle with a suspected fake plate, the server queries the same vehicle with the same license plate within a preset time period after determining the corresponding inbound information of the vehicle. And the vehicle with the same license plate has the same type as the current vehicle. Then the server obtains the characteristic information of the vehicles with the same license plate, compares the characteristic information of the vehicles with the same license plate, sorts the comparison results according to the similarity, outputs the results to a comparison result queue, calculates the average value of a plurality of similarity values in the comparison result queue, and finally compares the running speed and the time-space relationship of the vehicles with the same license plate.
And if the average value is not less than the preset similarity threshold value, the difference value of the running speeds is not less than the preset difference threshold value, and the space-time relationship is different, determining the vehicle and the vehicle as vehicles with the same license plate numbers running on different routes.
That is to say, within a preset time period, the vehicle information of the same license plate is inquired, and if the difference of the characteristic information is large, the vehicle information cannot be the same vehicle. Even if the characteristic information is accurately identified, and the traveling speed and the space-time relationship are combined, if the traveling speed and the space-time relationship are different greatly, two vehicles traveling on different routes cannot be the same vehicle.
In some implementations of the present application, in order to find out the fee evasion behavior of the cart or the cart logo, the image data of the vehicle after entering the highway is collected for analysis, and whether a suspected fee evasion behavior exists is judged.
Therefore, after the server determines the corresponding station entering information of the vehicle, the server searches the vehicle in the vehicle file library through the characteristic information, if the vehicle exists in the vehicle file library, the historical characteristic information of the vehicle in the vehicle file library is determined, and the vehicle passes through each sensing device and is analyzed, so that the characteristic information obtained by each analysis is compared with the historical characteristic information, if the characteristic information is inconsistent, the vehicle is determined as a suspected large-small-scale vehicle, namely, the behavior of fee evasion of the large-small-scale vehicle exists, and the ETC system gives an alarm.
If the vehicle does not exist in the vehicle archive, the vehicle is compared with toll collection vehicle types in ETC and OBU equipment, if the vehicle is inconsistent, the vehicle is determined to be a suspected large-vehicle small-mark vehicle, namely, a large-vehicle small-mark fee stealing behavior exists, and an alarm is given in an ETC system.
According to the embodiment of the application, the vehicle can be subjected to structural feature analysis and compared with feature information at the initial high speed through real-time monitoring in the driving process of the vehicle and every time when the vehicle passes through one sensing device, and suspected fee evasion behaviors exist if the vehicle does not accord with the feature information at the initial high speed.
Based on the same idea, some embodiments of the present application further provide a device and a non-volatile computer storage medium corresponding to the above method.
Fig. 2 is a schematic structural diagram of an expressway inspection device based on vehicle matching according to an embodiment of the present application, including:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
determining a vehicle structural model of the ETC system;
identifying the vehicle of the ETC lane through a vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time;
searching in the artificial charging system according to the characteristic information, and determining that the artificial charging system comprises the characteristic information and second station entering time;
and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
Some embodiments of the present application provide a non-volatile computer storage medium for vehicle matching based highway auditing, storing computer-executable instructions configured to: determining a vehicle structural model of the ETC system;
identifying the vehicle of the ETC lane through a vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time;
searching in the artificial charging system according to the characteristic information, and determining that the artificial charging system comprises the characteristic information and second station entering time;
and comparing the arrival time of the vehicle in the ETC system and the manual toll collection system, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
The embodiments in the present application are described in a progressive manner, and the same and similar parts among the embodiments can be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the device and media embodiments, the description is relatively simple as it is substantially similar to the method embodiments, and reference may be made to some descriptions of the method embodiments for relevant points.
The device and the medium provided by the embodiment of the application correspond to the method one to one, so the device and the medium also have the similar beneficial technical effects as the corresponding method, and the beneficial technical effects of the method are explained in detail above, so the beneficial technical effects of the device and the medium are not repeated herein.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The above description is only an example of the present application and is not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the technical principle of the present application shall fall within the protection scope of the present application.
Claims (10)
1. A vehicle matching-based highway inspection method is characterized by comprising the following steps:
determining a vehicle structural model of the ETC system;
identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time;
searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time;
and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
2. The method according to claim 1, wherein the identifying the vehicle in the ETC lane through the vehicle structural model and determining the characteristic information of the vehicle specifically comprise:
identifying the vehicle of the ETC lane through the vehicle structural model, and determining a detection area of the vehicle;
calculating the gray channels in the neighborhood of the pixels in the detection area and the gray channel of the background picture to determine a normalized correlation coefficient;
if the difference value between the numerical value of the normalized correlation coefficient and 1 is smaller than a preset threshold value, determining that the pixel is a shadow;
and eliminating the shadow to determine the vehicle type information of the vehicle.
3. The method of claim 2, wherein if the difference between the value of the normalized correlation coefficient and 1 is smaller than a predetermined threshold, after determining that the pixel is a shadow, the method further comprises:
inputting the pixel into a Gaussian mixture model;
and if the pixel is successfully matched with the Gaussian distribution of the Gaussian mixture model, finishing the verification that the pixel is a shadow.
4. The method according to claim 3, wherein determining the Gaussian mixture model specifically comprises:
obtaining a sample detection area;
establishing an initial Gaussian mixture model for each pixel in the sample detection area;
respectively updating the initial Gaussian mixture model according to the value of each pixel marked as the sample detection area to obtain a plurality of updated Gaussian mixture models;
determining an updated Gaussian mixture model corresponding to the Gaussian distribution with the maximum weight in the plurality of updated Gaussian mixture models;
determining the updated Gaussian mixture model as the Gaussian mixture model.
5. The method according to claim 1, wherein the identifying the vehicle in the ETC lane through the vehicle structural model and determining the characteristic information of the vehicle specifically comprise:
identifying the vehicle of the ETC lane through the vehicle structural model, and determining the license plate position of the vehicle;
selecting an area above the license plate position according to the license plate position; the upper region comprises a region not greater than a preset distance threshold;
and determining the body color of the vehicle according to the area.
6. The method according to claim 5, wherein the determining the color of the vehicle according to the region specifically comprises:
determining that the vehicle does not include a colored vehicle;
dividing the regions into N parts equally, and calculating the variance of each region in an S channel to obtain the S variance of each region;
determining an area corresponding to the S variance which is not greater than a preset variance threshold as an identification area;
carrying out color identification on the identification area, and determining the color of the identification area;
and voting the colors of the identification areas to determine the body color of the vehicle.
7. The method of claim 1, wherein after comparing the first arrival time with the second arrival time and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle, the method further comprises:
determining the vehicles with the same license plate corresponding to the vehicles within a preset time period; the vehicles with the same license plate number are the same as the license plate number of the vehicle;
comparing the characteristic information of the vehicle with the same license plate to obtain a comparison result of each characteristic information;
sorting the comparison results through the similarity, and determining a comparison result queue;
and comparing the running speeds and the space-time relationship of the vehicles and the vehicles with the same license plate according to the comparison result queue so as to identify the vehicles with the same license plate number in running on different routes.
8. The method of claim 1, wherein after comparing the first arrival time with the second arrival time and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle, the method further comprises:
retrieving the vehicle in a vehicle archive according to the characteristic information;
if the vehicle exists in the vehicle archive, determining historical characteristic information of the vehicle in the vehicle archive;
comparing the feature information with the historical feature information;
and if the vehicle is inconsistent, determining the vehicle as a suspect large-vehicle small-mark vehicle, and giving an alarm in the ETC system.
9. An expressway inspection apparatus based on vehicle matching, comprising:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to:
determining a vehicle structural model of the ETC system;
identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time;
searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time;
and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
10. A non-volatile computer storage medium for vehicle matching based highway auditing, the medium storing computer-executable instructions configured to:
determining a vehicle structural model of the ETC system;
identifying the vehicle of the ETC lane through the vehicle structural model, and determining the characteristic information of the vehicle and the first arrival time;
searching in a manual charging system according to the characteristic information, and determining that the manual charging system comprises the characteristic information and a second station entering time;
and comparing the first arrival time with the second arrival time, and determining the arrival information corresponding to the latest arrival time as the arrival information of the vehicle so as to charge the vehicle according to the arrival information.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110750681.3A CN113470206B (en) | 2021-07-01 | 2021-07-01 | Expressway inspection method, equipment and medium based on vehicle matching |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110750681.3A CN113470206B (en) | 2021-07-01 | 2021-07-01 | Expressway inspection method, equipment and medium based on vehicle matching |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113470206A true CN113470206A (en) | 2021-10-01 |
| CN113470206B CN113470206B (en) | 2023-04-25 |
Family
ID=77877521
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110750681.3A Active CN113470206B (en) | 2021-07-01 | 2021-07-01 | Expressway inspection method, equipment and medium based on vehicle matching |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113470206B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115240420A (en) * | 2022-07-23 | 2022-10-25 | 宁波市杭州湾大桥发展有限公司 | Highway auxiliary command method, system, storage medium and intelligent terminal |
| CN117314124A (en) * | 2023-11-28 | 2023-12-29 | 四川成渝高速公路股份有限公司 | Intelligent management system and method for expressway toll service |
| CN117373259A (en) * | 2023-12-07 | 2024-01-09 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574540A (en) * | 2014-12-24 | 2015-04-29 | 北京速通科技有限公司 | Electronic toll collection system and method |
| CN105551097A (en) * | 2015-12-09 | 2016-05-04 | 深圳市金溢科技股份有限公司 | Vehicular unit, vehicle positioning method and path identification method |
| CN105654562A (en) * | 2015-12-25 | 2016-06-08 | 中国航天系统工程有限公司 | System for highway electronic toll collection and method thereof |
| CN105761319A (en) * | 2016-02-18 | 2016-07-13 | 武汉万集信息技术有限公司 | Method and system for preventing ETC lane cheating |
| CN109147069A (en) * | 2018-07-05 | 2019-01-04 | 江苏恒宝智能系统技术有限公司 | A kind of highway paid system based on block chain |
| CN208477601U (en) * | 2018-02-12 | 2019-02-05 | 吉志勇 | The current mixing charging system of vehicle on highway |
| CN109543862A (en) * | 2017-09-21 | 2019-03-29 | 北京嘀嘀无限科技发展有限公司 | Network about vehicle order allocation method and network about vehicle Order splitting device |
| CN110197585A (en) * | 2019-04-29 | 2019-09-03 | 深圳市联和安业科技有限公司 | A kind of municipal intelligent traffic processing system and method based on TR-Min-Min algorithm |
| CN110335364A (en) * | 2019-05-07 | 2019-10-15 | 深圳华商云计算信息技术有限公司 | A kind of comprehensive porte-cochere charging system |
| CN111105514A (en) * | 2020-01-02 | 2020-05-05 | 招商华软信息有限公司 | Vehicle fee deduction method, device, system, equipment and storage medium |
| CN111340415A (en) * | 2020-02-14 | 2020-06-26 | 北京航空航天大学 | Vehicle spontaneous formation mode mining method based on freight track data |
| CN111932897A (en) * | 2020-09-23 | 2020-11-13 | 南京感动科技有限公司 | Method for fast iteration of highway toll inspection |
| CN112037350A (en) * | 2020-09-08 | 2020-12-04 | 广州市埃特斯通讯设备有限公司 | License plate identification method based on split RSU |
| CN112288197A (en) * | 2020-12-28 | 2021-01-29 | 盛威时代科技集团有限公司 | Intelligent scheduling method and device for station vehicles |
| CN112907965A (en) * | 2021-01-29 | 2021-06-04 | 陕西交通电子工程科技有限公司 | Road fee evasion auditing method and system based on traffic data middleboxes |
-
2021
- 2021-07-01 CN CN202110750681.3A patent/CN113470206B/en active Active
Patent Citations (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104574540A (en) * | 2014-12-24 | 2015-04-29 | 北京速通科技有限公司 | Electronic toll collection system and method |
| CN105551097A (en) * | 2015-12-09 | 2016-05-04 | 深圳市金溢科技股份有限公司 | Vehicular unit, vehicle positioning method and path identification method |
| CN105654562A (en) * | 2015-12-25 | 2016-06-08 | 中国航天系统工程有限公司 | System for highway electronic toll collection and method thereof |
| CN105761319A (en) * | 2016-02-18 | 2016-07-13 | 武汉万集信息技术有限公司 | Method and system for preventing ETC lane cheating |
| CN109543862A (en) * | 2017-09-21 | 2019-03-29 | 北京嘀嘀无限科技发展有限公司 | Network about vehicle order allocation method and network about vehicle Order splitting device |
| CN208477601U (en) * | 2018-02-12 | 2019-02-05 | 吉志勇 | The current mixing charging system of vehicle on highway |
| CN109147069A (en) * | 2018-07-05 | 2019-01-04 | 江苏恒宝智能系统技术有限公司 | A kind of highway paid system based on block chain |
| CN110197585A (en) * | 2019-04-29 | 2019-09-03 | 深圳市联和安业科技有限公司 | A kind of municipal intelligent traffic processing system and method based on TR-Min-Min algorithm |
| CN110335364A (en) * | 2019-05-07 | 2019-10-15 | 深圳华商云计算信息技术有限公司 | A kind of comprehensive porte-cochere charging system |
| CN111105514A (en) * | 2020-01-02 | 2020-05-05 | 招商华软信息有限公司 | Vehicle fee deduction method, device, system, equipment and storage medium |
| CN111340415A (en) * | 2020-02-14 | 2020-06-26 | 北京航空航天大学 | Vehicle spontaneous formation mode mining method based on freight track data |
| CN112037350A (en) * | 2020-09-08 | 2020-12-04 | 广州市埃特斯通讯设备有限公司 | License plate identification method based on split RSU |
| CN111932897A (en) * | 2020-09-23 | 2020-11-13 | 南京感动科技有限公司 | Method for fast iteration of highway toll inspection |
| CN112288197A (en) * | 2020-12-28 | 2021-01-29 | 盛威时代科技集团有限公司 | Intelligent scheduling method and device for station vehicles |
| CN112907965A (en) * | 2021-01-29 | 2021-06-04 | 陕西交通电子工程科技有限公司 | Road fee evasion auditing method and system based on traffic data middleboxes |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN115240420A (en) * | 2022-07-23 | 2022-10-25 | 宁波市杭州湾大桥发展有限公司 | Highway auxiliary command method, system, storage medium and intelligent terminal |
| CN115240420B (en) * | 2022-07-23 | 2023-06-13 | 宁波市杭州湾大桥发展有限公司 | Highway auxiliary command method, system, storage medium and intelligent terminal |
| CN117314124A (en) * | 2023-11-28 | 2023-12-29 | 四川成渝高速公路股份有限公司 | Intelligent management system and method for expressway toll service |
| CN117314124B (en) * | 2023-11-28 | 2024-02-27 | 四川成渝高速公路股份有限公司 | Intelligent management system and method for expressway toll service |
| CN117373259A (en) * | 2023-12-07 | 2024-01-09 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
| CN117373259B (en) * | 2023-12-07 | 2024-03-01 | 四川北斗云联科技有限公司 | Expressway vehicle fee evasion behavior identification method, device, equipment and storage medium |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113470206B (en) | 2023-04-25 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113470206B (en) | Expressway inspection method, equipment and medium based on vehicle matching | |
| CN109272034B (en) | Missing vehicle license plate prediction method and processing terminal | |
| CN109446881B (en) | Heterogeneous data-based highway section traffic state detection method | |
| CN111626277A (en) | Vehicle tracking method and device based on over-station inter-modulation index analysis | |
| CN116824862B (en) | Intelligent tunnel traffic operation control method, device and medium | |
| CN113221750A (en) | Vehicle tracking method, device, equipment and storage medium | |
| CN116229594A (en) | Vehicle charge monitoring method, device and medium based on expressway | |
| Ketcham et al. | Recognizing the Illegal Parking Patterns of Cars on the Road in Front of the Bus Stop Using the Support Vector Machine | |
| Hardjono et al. | Vehicle counting quantitative comparison using background subtraction, viola jones and deep learning methods | |
| Darapaneni et al. | Computer vision based license plate detection for automated vehicle parking management system | |
| CN114973659A (en) | Method, device and system for detecting indirect event of expressway | |
| Kejriwal et al. | Vehicle detection and counting using deep learning basedYOLO and deep SORT algorithm for urban traffic management system | |
| CN112863195B (en) | Vehicle state determination method and device | |
| KR101819568B1 (en) | The method for operating a parking management system according to recognition of electric vehicle and Hi-pass | |
| CN115909526B (en) | Expressway charging method and device | |
| CN114973681B (en) | In-transit vehicle sensing method and device | |
| CN116796024A (en) | Method and system for processing evidence chain of fee-escaping vehicle | |
| CN111709286A (en) | Vehicle sequencing and ETC transaction method, storage medium, industrial personal computer equipment and ETC system | |
| CN111915751B (en) | Roadside parking charging method, device, equipment and storage medium | |
| Patel et al. | Vehicle tracking and monitoring in surveillance video | |
| Haryono et al. | Accuracy in Object Detection based on Image Processing at the Implementation of Motorbike Parking on the Street | |
| CN115641728A (en) | ETC (electronic toll collection) inspection method, system, electronic equipment and medium | |
| CN117612115B (en) | Vehicle identification method based on expressway | |
| Pu et al. | A robust and real-time approach for license plate detection | |
| CN117312598B (en) | Evidence obtaining method, device, computer equipment and storage medium for fee evasion auditing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |