CN113689587B - Method and system for inspecting occupation of trunk roads in park - Google Patents
Method and system for inspecting occupation of trunk roads in park Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000007689 inspection Methods 0.000 claims abstract description 50
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- G—PHYSICS
- G07—CHECKING-DEVICES
- G07C—TIME OR ATTENDANCE REGISTERS; REGISTERING OR INDICATING THE WORKING OF MACHINES; GENERATING RANDOM NUMBERS; VOTING OR LOTTERY APPARATUS; ARRANGEMENTS, SYSTEMS OR APPARATUS FOR CHECKING NOT PROVIDED FOR ELSEWHERE
- G07C1/00—Registering, indicating or recording the time of events or elapsed time, e.g. time-recorders for work people
- G07C1/20—Checking timed patrols, e.g. of watchman
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- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B25/00—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems
- G08B25/01—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium
- G08B25/08—Alarm systems in which the location of the alarm condition is signalled to a central station, e.g. fire or police telegraphic systems characterised by the transmission medium using communication transmission lines
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Abstract
The application discloses a method and a system for inspecting occupation of trunk roads in a park, wherein the method comprises the following steps: the inspection robot detects vehicles on the trunk road in the park and determines the vehicles occupying the trunk road; shooting an image of the vehicle and sending the image to a back-end computing platform; the rear-end computing platform identifies the image and determines vehicle information of the vehicle; and searching the vehicle information in a vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner. According to the embodiment of the application, the patrol robot can flexibly patrol the trunk road of the park, the vehicles occupying the trunk road are found timely, the vehicle images are provided for the rear-end computing platform, so that the rear-end computing platform searches the vehicle owners corresponding to the vehicles, and timely carries out warning notification, the occupancy rate of personnel is low, and the patrol efficiency is high.
Description
Technical Field
The application relates to the technical field of intelligent parks, in particular to a method and a system for inspecting occupation of trunk roads in a park.
Background
The campus generally includes industrial park, logistics park, metropolitan industrial park, scientific and technological park, creative park, etc., and the scale of the park is generally relatively large, so that the park security work is a difficult task for park manager.
Along with intelligent promotion, when the function of patrolling and examining is carried out to the trunk road in the garden to the current wisdom garden project realization, the embodiment is just installing the camera more, then the camera is with the monitoring data in the garden uploading to the high in the clouds, ensures that the remote visible monitoring data of garden manager to and can call monitoring data again.
However, since a large number of cameras generate a large amount of monitoring data, a lot of manpower and material resources are consumed by the campus manager when processing the monitoring data, and therefore, the efficiency is low when the campus manager patrols and examines the trunk road in the campus.
Disclosure of Invention
The embodiment of the application provides a method and a system for inspecting the occupation of a trunk road in a park, which are used for solving the problem of low inspection efficiency of the trunk road in the park.
The embodiment of the application adopts the following technical scheme:
in one aspect, an embodiment of the present application provides a method for inspecting occupancy of a trunk road in a campus, where the method includes: the inspection robot detects vehicles on the trunk road in the park and determines the vehicles occupying the trunk road; shooting an image of the vehicle and sending the image to a back-end computing platform; the rear-end computing platform identifies the image and determines vehicle information of the vehicle; and searching the vehicle information in a vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner.
In one example, the capturing an image of the vehicle and transmitting the image to a back-end computing platform specifically includes: shooting the vehicle, and determining a first image of the vehicle; identifying the first image and determining coordinates of the vehicle in the first image; determining a second image of the vehicle according to a preset time interval; identifying the second image and determining coordinates of the vehicle in the second image; judging whether the coordinates in the first image are consistent with the coordinates in the second image; if yes, the second image is sent to a back-end computing platform.
In one example, the determining whether the coordinates in the first image and the coordinates in the second image are consistent specifically includes: judging whether the coordinates in the first image are consistent with the coordinates in the second image; if not, determining a third image of the vehicle in the preset time interval; identifying the third image and determining coordinates of the vehicle in the third image; judging whether the coordinates in the second image are consistent with the coordinates in the third image; if yes, the third image is sent to a back-end computing platform; wherein, the inspection robot is in a static state.
In one example, the determining whether the coordinates in the first image and the coordinates in the second image are consistent specifically includes: judging whether the coordinates in the first image are consistent with the coordinates in the second image; if not, determining a third image of the vehicle according to the preset time interval; identifying the third image and determining coordinates of the vehicle in the third image; judging whether the coordinates in the second image are consistent with the coordinates in the third image; if yes, the third image is sent to a back-end computing platform.
In one example, if so, the sending the third image to the back-end computing platform specifically includes: determining that the inspection robot comprises an alarm and a speed sensor; if yes, determining a lamp characteristic area image of the vehicle from the third image; identifying the characteristic area image of the car lamp, and determining that the car is in emergency stop; alarming and prompting the vehicle through the alarm, and acquiring the speed information of the vehicle through the speed sensor within a preset time length; judging whether the vehicle moves or not according to the speed information; and if not, sending the third image to a back-end computing platform.
In one example, the inspection robot performs vehicle detection on a trunk road in a park, and determines a vehicle occupying the trunk road, and specifically includes: the inspection robot acquires a preset navigation route inspected in a park; and based on the navigation route, detecting vehicles on the trunk road in the park, and determining the vehicles occupying the trunk road.
In one example, after the capturing the image of the vehicle and transmitting the image to a back-end computing platform, the method further comprises: determining that the inspection robot comprises a positioning module; determining position information of the vehicle through the positioning module, and sending the position information to the back-end computing platform; and the back-end computing platform determines corresponding park managers according to the position information and sends the position information to the corresponding park managers.
In one example, the determining, by the positioning module, the location information of the vehicle specifically includes: determining position information of the inspection robot through the positioning module; judging whether the image comprises a marker; if not, taking the position information of the inspection robot as the position information of the vehicle; if yes, determining the position information of the marker through a pre-constructed park geographic information model; determining relative coordinates of the marker and the vehicle in the image; and determining the position information of the vehicle according to the position information of the identifier and the relative coordinates.
In one example, the back-end computing platform determines, according to the location information, a corresponding campus manager, including: positioning a park manager according to a mobile terminal carried by the park manager to determine the position information of the park manager; comparing the position information of the park manager with the position information of the vehicle to determine the park manager nearest to the vehicle; and taking the park manager closest to the vehicle as the corresponding park manager.
In one example, the back-end computing platform identifies the image, and determines vehicle information of the vehicle, which specifically includes: the back-end computing platform determines a pre-constructed vehicle structural model; identifying the image according to the vehicle structural model, and determining a license plate characteristic region image of the vehicle; and identifying the license plate characteristic region image and determining the license plate number of the vehicle.
On the other hand, the embodiment of the application provides a system for inspecting the occupation of a trunk road in a park, wherein the system comprises: the inspection robot detects vehicles on the trunk road in the park and determines the vehicles occupying the trunk road; shooting an image of the vehicle and sending the image to a back-end computing platform; the rear-end computing platform identifies the image and determines vehicle information of the vehicle; and searching the vehicle information in a vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner.
The above-mentioned at least one technical scheme that this application embodiment adopted can reach following beneficial effect:
according to the embodiment of the application, the patrol robot can flexibly patrol the trunk road of the park, the vehicles occupying the trunk road are found timely, the vehicle images are provided for the rear-end computing platform, so that the rear-end computing platform searches the vehicle owners corresponding to the vehicles, and timely carries out warning notification, the occupancy rate of personnel is low, and the patrol efficiency is high.
Drawings
In order to more clearly illustrate 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 diagram of a framework of a system for inspecting occupancy of a trunk road in a campus according to an embodiment of the present application;
fig. 2 is a schematic flow chart of a method for inspecting occupancy of a trunk road in a campus according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of a trunk occupancy patrol equipment in a campus provided in an embodiment of the present application.
Detailed Description
For the purposes, technical solutions and advantages of the present application, the technical solutions of the present application will be clearly and completely described below with reference to specific embodiments and corresponding drawings. It will be apparent that the described embodiments are only some, but not all, of the embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
Some embodiments of the present application are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of a framework of a system for inspecting occupancy of a trunk road in a campus according to an embodiment of the present application.
As shown in fig. 1, the system for inspecting the occupancy of the trunk road in the campus at least comprises an inspection robot 100, a back-end computing platform 200, a vehicle registration information database 300 and a mobile terminal 400.
The inspection robot 100, the vehicle registration information database 300, and the mobile terminal 400 are all connected to the back-end computing platform 200.
The inspection robot 100 comprises a lane occupation identification module and a photographing module, the inspection robot 100 stops when finding that the vehicle occupies a lane through the lane occupation identification module, the photographing module is triggered to photograph an image of the lane occupation vehicle, and the inspection robot 100 sends the image of the vehicle to the rear end computing platform 200. The track occupation identification module is implemented by software, and the photographing module is equipment with a photographing function.
The setting position of the photographing module on the inspection robot 100 may be set according to actual needs, which is not limited herein.
The back-end computing platform 200 is a server, which may be a single device, or may be a system composed of multiple devices, i.e., a distributed server, which is not specifically limited in this application.
The vehicle registration information database 300 is used to store registration information of vehicles entering the park, including information related to the vehicle owners, such as a cell phone number, a license plate number, a time of entering the park, and the like.
The mobile terminal 400 is carried by a campus manager, and a GPS positioning module is set in the mobile terminal 400 for acquiring the position information of the campus manager. For example, the mobile terminal 400 is an electronic wristband, and when a campus manager carries the electronic wristband, the location information of the campus manager in the campus is obtained.
In some embodiments of the present application, the inspection robot 100 performs vehicle detection on the trunk in the campus through the trunk-occupation recognition module, determines a vehicle occupying the trunk, then triggers the photographing module, photographs an image of the vehicle, and sends the image to the back-end computing platform 200.
The rear end computing platform recognizes the vehicle image uploaded by the inspection robot 100, thereby obtaining vehicle information of the vehicle. The vehicle information refers to characteristic information of the appearance of the vehicle, such as information of a vehicle type, a vehicle lamp, a license plate number and the like.
Then, the back-end computing platform 100 retrieves the vehicle information from the vehicle registration information database 300, searches for the owner information of the vehicle, and then sends warning information to the owner to remind the vehicle of occupying the road. For example, the vehicle owner information is queried from the vehicle registration information database 300 by the license plate number of the vehicle.
How the inspection robot 100 and the back-end computing platform 200 manage the trunk occupancy vehicles in the campus will be described in detail with reference to fig. 2 and related content, as shown in fig. 2.
Fig. 2 is a schematic flow chart of a method for inspecting occupancy of a trunk road in a campus according to an embodiment of the present application.
S201: and the inspection robot detects vehicles on the trunk road in the park and determines the vehicles occupying the trunk road.
Specifically, the inspection robot 100 further includes a navigation module and a vehicle detection module, the inspection robot 100 obtains a preset navigation route for inspection in the campus through the navigation module, and performs vehicle detection on the trunk road in the campus according to the navigation route through the vehicle detection module to determine a vehicle occupying the trunk road. That is, the navigation module provides the inspection robot 100 with a prescribed navigation route, that is, a main road, and the inspection robot 100 performs vehicle detection on the main road in real time.
It should be noted that, how the embodiments of the present application specifically perform vehicle detection on the trunk road in the campus may be set according to actual needs, which is not limited herein specifically.
S202: an image of the vehicle is taken and sent to a back-end computing platform.
In some embodiments of the present application, when the vehicle occupies the trunk at the current time, there is a situation that the vehicle owner can drive the vehicle away next time, and at this time, the back-end computing platform 200 does not need to inform the vehicle owner to move the vehicle. Therefore, in order to save more resources, the inspection robot 100 determines the movement condition of the vehicle for a preset period of time.
Specifically, after detecting the vehicle, the inspection robot 100 will stop at the current position, take a first image of the vehicle, identify the first image, and determine the coordinates of the vehicle in the first image. And then, according to a preset time interval, shooting for the second time at the current position, determining a second image of the vehicle, identifying the first image, and determining the coordinates of the vehicle in the first image. And finally, judging whether the coordinates in the first image are consistent with the coordinates in the second image, and if so, sending the second image to a back-end computing platform.
That is, the inspection robot 100 performs the secondary photographing at the current position of the stop according to the preset time interval, respectively, and if the coordinates of the vehicle in the two images are identical, it is indicated that the vehicle is not moving.
When the vehicle occupies the trunk road at the current time, the vehicle owner moves the vehicle at the next moment, but the vehicle is stopped at the next moment. For example, when the inspection robot 100 detects a vehicle occupying a trunk, the vehicle owner is parking but not, and some movement is required to occur and then parking is performed.
Therefore, when the coordinates in the first image are inconsistent with the coordinates in the second image, shooting is performed for the third time at the current position according to the preset time interval, a third image of the vehicle is determined, the third image is identified, and the coordinates of the vehicle in the third image are determined. And finally, judging whether the coordinates in the second image are consistent with the coordinates in the third image, if not, not sending the third image to the back-end computing platform, namely, not processing the detected vehicle occupying the trunk road.
In the second photographing, the vehicle is not included in the image that may be photographed, that is, the vehicle has been turned on, and then the coordinates of the vehicle in the two images are inconsistent.
Further, if the coordinates in the second image are consistent with the coordinates in the third image, it is indicated that the vehicle is not moving after the inspection robot 100 performs the second photographing. The third image may be sent to the back-end computing platform 200.
That is, the inspection robot 100 can screen out the vehicle image to be transmitted to the rear end computing platform 300 more accurately after the third photographing, but since people often start to stop the vehicle in emergency, there is a case where the vehicle owner performs emergency stop, and if this is the case, the time is short, and the warning process for the vehicle is not necessary.
Therefore, the inspection robot 100 further includes an alarm and a speed sensor, and when the coordinates in the second image are consistent with the coordinates in the third image, the inspection robot 100 determines a lamp feature area image of the vehicle from the third image, and then identifies the lamp feature area image, so as to determine that the vehicle is in an emergency stop state, for example, when the lamp of the vehicle is in a double flashing state. At this time, the inspection robot 100 gives an alarm prompt to the vehicle through the alarm, obtains the speed information of the vehicle through the speed sensor within a preset time period, judges whether the vehicle moves according to the speed information, if not, sends the third image to the rear end computing platform, and if not, does not send the third image to the rear end computing platform.
S203: the rear-end computing platform identifies the image and determines vehicle information of the vehicle.
In some embodiments of the present application, since the license plate number of the vehicle is unique and is bound to the vehicle owner, the efficiency of searching for the vehicle owner is high, so the back-end computing platform 200 identifies the image according to the pre-constructed vehicle structural model, determines the license plate feature area image of the vehicle, and then identifies the license plate feature area image to determine the license plate number of the vehicle.
S204: and searching the vehicle information in the vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner.
The warning information comprises position information for informing the vehicle owner of the vehicle and prompt information for prompting the vehicle owner to move the vehicle.
In some embodiments of the present application, the intervention of a park manager is required to warn the owner of the vehicle to move the vehicle because some owners cannot move the vehicle in time.
Therefore, the inspection robot 100 further includes a positioning module, after the inspection robot 100 sends the image to the back-end computing platform 200, the positioning module determines the position information of the vehicle, sends the position information to the back-end computing platform 200, and the back-end computing platform 200 determines the corresponding campus manager according to the position information and sends the position information to the corresponding campus manager. Wherein, the warning information of occupying the trunk road vehicles can also be sent to the corresponding park manager
Further, in order to more accurately determine the position information of the vehicle, the inspection robot 100 first determines its own position information through the positioning module, and then uploads to the back-end computing platform 200 whether the identifier is included in the vehicle image. Wherein the marker refers to other buildings except vehicles in the image.
If not, the position information of the inspection robot 100 is used as the position information of the vehicle. The location information is notified to the campus manager for on-site processing, so that the existence of errors in the location does not substantially affect the final judgment.
If so, determining the position information of the marker through a pre-constructed park geographic information model, determining the relative coordinates of the marker and the vehicle in the image, and finally determining the position information of the vehicle according to the position information and the relative coordinates of the marker.
Still further, since the range of the campus is large, the range of the activity of the campus manager is also large, and therefore, in order to improve the efficiency of taking relevant measures to the vehicles occupying the trunk in time, the inspection robot 100 positions the campus manager according to the mobile terminal 400 carried by the campus manager, so as to determine the position information of the campus manager. Then, the location information of the park manager is compared with the location information of the vehicle, the park manager closest to the vehicle is determined, and the park manager closest to the vehicle is set as the corresponding park manager. That is, the location information of the vehicle occupying the thoroughfare is transmitted to the park manager nearest to the vehicle.
It should be noted that, although the embodiment of the present application is described with reference to fig. 2 to sequentially describe steps S201 to S204, this does not represent that steps S201 to S204 must be performed in strict order. The steps S201 to S204 are sequentially described according to the sequence shown in fig. 2 in order to facilitate the understanding of the technical solution of the embodiment of the present application by those skilled in the art. In other words, in the embodiment of the present application, the sequence between step S201 to step S204 may be appropriately adjusted according to the actual needs.
Through the method in fig. 2, the embodiment of the application can flexibly patrol the trunk road of the park through the patrol robot, discover vehicles occupying the trunk road in time, provide vehicle images for the back-end computing platform, so that the back-end computing platform searches the vehicle owners corresponding to the vehicles, timely carries out warning notification, has low personnel occupancy rate and high patrol efficiency.
Based on the same thought, some embodiments of the present application further provide a device and a non-volatile computer storage medium corresponding to the above method.
Fig. 3 is a schematic structural diagram of a device for inspecting arterial road occupation in a campus, which is provided in an embodiment of the present application, and is applied to an inspection robot, where the device includes:
at least one processor; the method comprises the steps of,
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:
detecting vehicles on the trunk road in the park, and determining vehicles occupying the trunk road;
shooting an image of a vehicle and sending the image to a back-end computing platform; the rear-end computing platform is used for identifying the image and determining vehicle information of the vehicle; and searching the vehicle information in the vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner.
Some embodiments of the present application provide a non-volatile computer storage medium for inspecting arterial road occupancy in a campus, storing computer executable instructions, and applying the computer executable instructions to an inspection robot, where the computer executable instructions are configured to:
detecting vehicles on the trunk road in the park, and determining vehicles occupying the trunk road;
shooting an image of a vehicle and sending the image to a back-end computing platform; the rear-end computing platform is used for identifying the image and determining vehicle information of the vehicle; and searching the vehicle information in the vehicle registration information database, and determining the owner information corresponding to the vehicle so as to send warning information to the owner.
All embodiments in the application are described in a progressive manner, and identical and similar parts of all embodiments are mutually referred, so that each embodiment mainly describes differences from other embodiments. In particular, for the apparatus and medium embodiments, the description is relatively simple, as it is substantially similar to the method embodiments, with reference to the section of the method embodiments being relevant.
The devices and media provided in the embodiments of the present application are in one-to-one correspondence with the methods, so that the devices and media also have similar beneficial technical effects as the corresponding methods, and since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media are not described in detail herein.
It will be appreciated by those skilled in the art that 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 flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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 one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of computer-readable media.
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 storage media for a computer 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, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
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 one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises the element.
The foregoing is merely exemplary of the present application and is not intended to limit the present application. Various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the technical principles of the present application should fall within the protection scope of the present application.
Claims (4)
1. The method for inspecting the occupation of the trunk road in the campus is characterized by comprising the following steps:
the inspection robot detects vehicles on the trunk road in the park and determines the vehicles occupying the trunk road;
shooting an image of the vehicle and sending the image to a back-end computing platform;
the rear-end computing platform identifies the image and determines vehicle information of the vehicle;
retrieving the vehicle information in a vehicle registration information database, and determining owner information corresponding to the vehicle so as to send warning information to the owner;
the capturing an image of the vehicle and transmitting the image to a back-end computing platform specifically includes:
shooting the vehicle, and determining a first image of the vehicle;
identifying the first image and determining coordinates of the vehicle in the first image;
determining a second image of the vehicle according to a preset time interval;
identifying the second image and determining coordinates of the vehicle in the second image;
judging whether the coordinates in the first image are consistent with the coordinates in the second image;
if yes, the second image is sent to a back-end computing platform;
wherein the inspection robot is in a static state;
the determining whether the coordinates in the first image are consistent with the coordinates in the second image specifically includes:
judging whether the coordinates in the first image are consistent with the coordinates in the second image;
if not, determining a third image of the vehicle according to the preset time interval;
identifying the third image and determining coordinates of the vehicle in the third image;
judging whether the coordinates in the second image are consistent with the coordinates in the third image;
if yes, the third image is sent to a back-end computing platform;
if yes, the third image is sent to a back-end computing platform, and the method specifically comprises the following steps:
determining that the inspection robot comprises an alarm and a speed sensor;
if yes, determining a lamp characteristic area image of the vehicle from the third image;
identifying the characteristic area image of the car lamp, and determining that the car is in emergency stop;
alarming and prompting the vehicle through the alarm, and acquiring the speed information of the vehicle through the speed sensor within a preset time length;
judging whether the vehicle moves or not according to the speed information;
if not, the third image is sent to a back-end computing platform;
after capturing an image of the vehicle and transmitting the image to a back-end computing platform, the method further comprises:
determining that the inspection robot comprises a positioning module;
determining position information of the vehicle through the positioning module, and sending the position information to the back-end computing platform;
the back-end computing platform determines corresponding park managers according to the position information and sends the position information to the corresponding park managers;
the determining, by the positioning module, the position information of the vehicle specifically includes:
determining position information of the inspection robot through the positioning module;
judging whether the image comprises a marker;
if not, taking the position information of the inspection robot as the position information of the vehicle;
if yes, determining the position information of the marker through a pre-constructed park geographic information model;
determining relative coordinates of the marker and the vehicle in the image;
and determining the position information of the vehicle according to the position information of the identifier and the relative coordinates.
2. The method according to claim 1, wherein the inspection robot performs vehicle detection on a trunk in a campus, and determines vehicles occupying the trunk, and specifically comprises:
the inspection robot acquires a preset navigation route inspected in a park;
and based on the navigation route, detecting vehicles on the trunk road in the park, and determining the vehicles occupying the trunk road.
3. The method according to claim 1, wherein the back-end computing platform determines the corresponding campus manager according to the location information, and specifically comprises:
positioning a park manager according to a mobile terminal carried by the park manager to determine the position information of the park manager;
comparing the position information of the park manager with the position information of the vehicle to determine the park manager nearest to the vehicle;
and taking the park manager closest to the vehicle as the corresponding park manager.
4. The method according to claim 1, wherein the back-end computing platform identifies the image and determines vehicle information of the vehicle, specifically comprising:
the back-end computing platform determines a pre-constructed vehicle structural model;
identifying the image according to the vehicle structural model, and determining a license plate characteristic region image of the vehicle;
and identifying the license plate characteristic region image and determining the license plate number of the vehicle.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110982362.5A CN113689587B (en) | 2021-08-25 | 2021-08-25 | Method and system for inspecting occupation of trunk roads in park |
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015011708A1 (en) * | 2013-07-26 | 2015-01-29 | Anagog Ltd. | Parking occupancy estimation |
| CN105180951A (en) * | 2014-06-05 | 2015-12-23 | 宝马股份公司 | Route Planning For Vehicle |
| WO2018227241A1 (en) * | 2017-06-15 | 2018-12-20 | Ubipark Pty Ltd | Computer-implemented systems for locating a vehicular parking space and controlling access thereto |
| CN109255957A (en) * | 2018-11-20 | 2019-01-22 | 湖北文理学院 | The method and system of vehicle driving monitoring in a kind of tunnel |
| DE102018008177A1 (en) * | 2018-10-16 | 2019-04-11 | Daimler Ag | Method for detecting parking lot information |
| CN112380948A (en) * | 2020-11-09 | 2021-02-19 | 济南骏驰企业管理咨询有限公司 | Training method and system for object re-recognition neural network and electronic equipment |
| CN112669510A (en) * | 2020-12-16 | 2021-04-16 | 日立楼宇技术（广州）有限公司 | Park brake control method, device, equipment and storage medium |
| KR102254767B1 (en) * | 2020-11-19 | 2021-05-24 | 한국건설기술연구원 | Traffic volume survey apparatus and method based on vehicle image recognition |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7355527B2 (en) * | 2005-01-10 | 2008-04-08 | William Franklin | System and method for parking infraction detection |
| US20110140927A1 (en) * | 2009-12-14 | 2011-06-16 | Kevin Lawrence Lee | Method and apparatus for avoiding parking violations |
| US8682036B2 (en) * | 2012-04-06 | 2014-03-25 | Xerox Corporation | System and method for street-parking-vehicle identification through license plate capturing |
| US8830088B2 (en) * | 2012-12-18 | 2014-09-09 | TCS International, Inc. | Zone controller |
| US9666075B2 (en) * | 2013-11-18 | 2017-05-30 | ImageMaker Development Inc. | Automated parking space management system with dynamically updatable display device |
| CN107622666B (en) * | 2016-07-13 | 2020-11-24 | 尚艳燕 | Balance car cruise law enforcement method and device |
| CN106205144B (en) * | 2016-09-07 | 2018-06-19 | 东南大学 | Highway Emergency Vehicle Lane occupies supervision punishment method and system |
| US20180285846A1 (en) * | 2017-04-03 | 2018-10-04 | General Motors Llc | System and method for parking violation risk management |
| CN109559518A (en) * | 2018-12-10 | 2019-04-02 | 安徽四创电子股份有限公司 | A kind of novel intelligent traffic block port based on structured image recognizer |
| CN109686103B (en) * | 2018-12-29 | 2021-06-08 | 中南大学 | Intelligent traffic violation monitoring and management system and method based on image recognition |
| CN109948436B (en) * | 2019-02-01 | 2020-12-08 | 深兰科技（上海）有限公司 | Method and device for monitoring vehicles on road |
| CN111292556B (en) * | 2020-01-22 | 2022-03-01 | 长安大学 | Vehicle early warning system and method based on roadside double-flash-lamp recognition |
| CN112489434A (en) * | 2020-12-25 | 2021-03-12 | 重庆市市政设计研究院有限公司 | RFID-based adaptive vehicle illegal parking monitoring law enforcement device |
| CN112819989A (en) * | 2021-01-19 | 2021-05-18 | 杭州海康威视数字技术股份有限公司 | Park patrol method, device and equipment |
-
2021
- 2021-08-25 CN CN202110982362.5A patent/CN113689587B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2015011708A1 (en) * | 2013-07-26 | 2015-01-29 | Anagog Ltd. | Parking occupancy estimation |
| CN105180951A (en) * | 2014-06-05 | 2015-12-23 | 宝马股份公司 | Route Planning For Vehicle |
| WO2018227241A1 (en) * | 2017-06-15 | 2018-12-20 | Ubipark Pty Ltd | Computer-implemented systems for locating a vehicular parking space and controlling access thereto |
| DE102018008177A1 (en) * | 2018-10-16 | 2019-04-11 | Daimler Ag | Method for detecting parking lot information |
| CN109255957A (en) * | 2018-11-20 | 2019-01-22 | 湖北文理学院 | The method and system of vehicle driving monitoring in a kind of tunnel |
| CN112380948A (en) * | 2020-11-09 | 2021-02-19 | 济南骏驰企业管理咨询有限公司 | Training method and system for object re-recognition neural network and electronic equipment |
| KR102254767B1 (en) * | 2020-11-19 | 2021-05-24 | 한국건설기술연구원 | Traffic volume survey apparatus and method based on vehicle image recognition |
| CN112669510A (en) * | 2020-12-16 | 2021-04-16 | 日立楼宇技术（广州）有限公司 | Park brake control method, device, equipment and storage medium |
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