CN112017444A - Fake-licensed vehicle detection method and device, medium and system thereof - Google Patents
Fake-licensed vehicle detection method and device, medium and system thereof Download PDFInfo
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- CN112017444A CN112017444A CN202010886003.5A CN202010886003A CN112017444A CN 112017444 A CN112017444 A CN 112017444A CN 202010886003 A CN202010886003 A CN 202010886003A CN 112017444 A CN112017444 A CN 112017444A
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
- G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
Abstract
The application relates to the field of intelligent traffic, in particular to a fake-licensed vehicle detection method, a device, a medium and a system thereof. The fake-licensed vehicle detection method comprises the following steps: acquiring vehicle image data of a vehicle with a license plate to be detected, and acquiring vehicle position information of the vehicle with the license plate to be detected; carrying out feature extraction on vehicle image data of a vehicle with a license plate to be detected to obtain vehicle feature information; determining the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data; and when detecting that the license plate vehicle to be detected has at least two vehicle tracks at the same time, determining that the license plate vehicle to be detected is a suspected fake-licensed vehicle. Therefore, the fake-licensed vehicles can be detected in real time and at any place, and the detection rate of the fake-licensed vehicles is improved.
Description
Technical Field
The application relates to the field of intelligent traffic, in particular to a fake-licensed vehicle detection method, a device, a medium and a system thereof.
Background
In recent years, along with the rapid increase of the motor vehicle holding amount in China, the illegal act of vehicle registration is in an increasing situation. The fake license plate refers to the installation of a fake license plate or the application of license plates of other vehicles. The fake-licensed behavior has negative effects on the aspects of public transportation management, public safety maintenance and the like. The fake-licensed vehicle can be found by fake-licensed detection so as to maintain the order of public transportation.
In the prior art, whether the fake-licensed vehicle is detected is generally judged by a motor vehicle through the minimum passing time between the two checkpoints, and if the time difference of one vehicle shot at the two checkpoints is smaller than the minimum passing time between the two checkpoints, one vehicle is judged to be the fake-licensed vehicle. The method requires that the fake-licensed vehicle and the real vehicle need to be subjected to bayonet detection, and if one of the fake-licensed vehicle and the real vehicle is not subjected to bayonet detection within a period of time, the fake-licensed phenomenon cannot be judged.
Disclosure of Invention
The embodiment of the application provides a fake-licensed vehicle detection method, a fake-licensed vehicle detection device, a fake-licensed vehicle detection medium and a fake-licensed vehicle detection system. With the improvement of living standard and the development of video monitoring technology, vehicles on roads are more and more, and how to maintain the order of public transportation through the video monitoring technology and avoid illegal behaviors becomes a problem which is more and more concerned by people. The fake-licensed vehicle detection method provided by the application analyzes the image information collected by the monitoring camera, determines the vehicle running track of the fake-licensed vehicle through the vehicle characteristic information, the vehicle position information and the vehicle image data collection time information of the fake-licensed vehicle when the fake-licensed vehicle appears, and can realize fake-licensed vehicle detection when the fake-licensed vehicle does not pass through a bayonet detection area, so that the fake-licensed vehicle can be detected in real time and at any place, and the detection rate of the fake-licensed vehicle is improved.
In a first aspect, an embodiment of the present application provides a fake-licensed vehicle detection method, including: acquiring vehicle image data of a vehicle with a license plate to be detected, and acquiring vehicle position information of the vehicle with the license plate to be detected; carrying out feature extraction on vehicle image data of a vehicle with a license plate to be detected to obtain vehicle feature information; determining the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data; and when detecting that the license plate vehicle to be detected has at least two vehicle tracks at the same time, determining that the license plate vehicle to be detected is a suspected fake-licensed vehicle.
In a possible implementation of the first aspect, the method further includes: inquiring a mapping relation table; and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle.
In a possible implementation of the first aspect, the method further includes: the vehicle position information is latitude and longitude coordinates or a position area of the vehicle.
In a possible implementation of the first aspect, the method further includes: and judging whether the vehicle attribute information or the vehicle occupant information in the vehicle image data with at least two vehicle tracks at the same time is the same, and further determining that the license plate vehicle to be detected is a fake plate vehicle under the condition that the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data is different.
In a possible implementation of the first aspect, the method further includes: the behavior trajectory information of the object is composed of the acquisition time of the image data of the object within a period of time and the position information of the object.
In a possible implementation of the first aspect, the method further includes: determining the behavior trace of the object corresponding to the object mark comprises tracking the behavior trace of the object, setting a trigger condition, and calculating a trigger characteristic and a probability.
In a second aspect, an embodiment of the present application provides a fake-licensed vehicle detection device, including: the data acquisition module is used for acquiring image data;
and the data acquisition module is used for acquiring the vehicle image data and the vehicle position information of the vehicle with the license plate to be detected.
And the characteristic extraction module is used for extracting the characteristics of the vehicle image data of the vehicle with the license plate to be detected to obtain the vehicle characteristic information.
And the behavior track determining module is used for determining the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data.
And the fake-licensed vehicle determining module is used for determining that the fake-licensed vehicle to be detected is a suspected fake-licensed vehicle if at least two vehicle tracks of the fake-licensed vehicle to be detected exist at the same time.
In a possible implementation of the second aspect, the fake-licensed vehicle determination module is further configured to query a mapping relation table;
and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle.
In a possible implementation of the second aspect, the behavior trace determining module is further configured to determine the vehicle location information as longitude and latitude coordinates or a location area of the vehicle.
In a possible implementation of the second aspect, the fake-licensed vehicle determining module is further configured to determine whether vehicle attribute information or vehicle occupant information in vehicle image data in which at least two vehicle trajectories exist at the same time is the same, and further determine that the license plate vehicle to be detected is a suspected fake-licensed vehicle if the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data is different.
In a third aspect, the present application provides a machine-readable medium having instructions stored thereon, which when executed on a machine, cause the machine to perform the method for detecting a fake-licensed vehicle in the first aspect and possible implementations of the first aspect.
In a fourth aspect, an embodiment of the present application provides a system, including:
a memory for storing instructions for execution by one or more processors of the system, an
And the processor is one of the processors of the system and is used for executing the fake-licensed vehicle detection method in the first aspect and possible implementations of the first aspect.
Drawings
FIG. 1 illustrates a fake-licensed vehicle detection scenario, according to some embodiments of the present application;
FIG. 2 illustrates a flow chart of a method of fake-licensed vehicle detection, according to some embodiments of the present application;
FIG. 3 illustrates a block diagram of a fake-licensed vehicle detection device, according to some embodiments of the present application;
FIG. 4 illustrates a block diagram of a system, according to some embodiments of the present application;
fig. 5 illustrates a block diagram of a system on a chip (SoC), according to some embodiments of the present application.
Detailed Description
Illustrative embodiments of the present application include, but are not limited to, a fake-licensed vehicle detection method, apparatus, medium, and system thereof.
Embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
With the development of artificial intelligence combined with big data analysis technology, the application of detecting security or abnormal behaviors of objects in life by collecting behavior information of the objects such as vehicles, pedestrians and the like through a monitoring camera is more and more extensive. When the object group has a certain behavior rule, the abnormal behavior of the individual can be reversely deduced through the common behavior rule characteristics of the group. The following describes in detail a scenario in which the present application is applied, taking an object as a vehicle and an abnormal object as fake-licensed vehicle detection as an example, with reference to fig. 1.
According to some embodiments of the present application, an application scenario of a fake-licensed vehicle detection method is shown. In the scene shown in fig. 1, the scene includes an image data acquisition device 11, a server 12 and a vehicle 13, the image data acquisition device 11 is a device having a function of acquiring image data, and although the camera 11 is shown in the figure, the device suitable for acquiring image data may also be other electronic devices having a camera function, but is not limited thereto. The camera 11 is used for collecting image data, and the camera 11 is a camera at each place, collects pictures of vehicles 13 at each place, and sends the image data to the server 12. The server 12 is configured to receive the captured image data sent by the camera 11, where the vehicle image data includes the vehicle image data capturing time information and the vehicle position information. The server 12 performs feature extraction on the vehicle image data of the to-be-detected license plate vehicle through a convolutional neural network algorithm to obtain vehicle feature information, determines the vehicle running track of the to-be-detected license plate vehicle according to the vehicle feature information of the to-be-detected license plate vehicle and the acquisition time information of the vehicle image data of the to-be-detected license plate vehicle, and the server 12 determines the abnormal detection result of the vehicle according to the vehicle running track of the to-be-detected license plate vehicle. And if two or more vehicles with the same license plate have two or more tracks in the same time period, judging that the vehicle to be detected is a suspected fake-plate vehicle. Specifically, the method comprises the following steps: detecting a mapping relation between the acquisition time of vehicle image data of a to-be-detected license plate vehicle and vehicle position information in a vehicle running track of the to-be-detected license plate vehicle, and inquiring the mapping relation table; and if the mapping relation table contains two or more pieces of vehicle track information corresponding to the to-be-detected license plate vehicles in the same time period and the to-be-detected license plate vehicles are different from the vehicle attribute information or the vehicle occupant information of the two or more vehicles, judging that the to-be-detected license plate vehicles are suspected fake-licensed vehicles.
In the embodiment of the present application, the server 12 and the camera 11 may be connected through a wireless link or a wired link.
It is understood that the fake-licensed vehicle detection scenario shown in fig. 1 is only one example of a scenario for implementing the embodiment of the present application, and the embodiment of the present application is not limited to the scenario shown in fig. 1. In other embodiments, the scenario illustrated in FIG. 1 may include more or fewer devices or components than the illustrated embodiment, or some components may be combined, some components may be split, or a different arrangement of components.
FIG. 2 illustrates a flow chart of a method of fake-licensed vehicle detection, according to some embodiments of the present application. The method for detecting a fake-licensed vehicle provided by the present application is described in detail below with reference to fig. 1 to 2. As shown in fig. 2, specifically, the method includes:
1) the server 12 obtains vehicle image data of a vehicle with a license plate to be detected, and obtains vehicle position information (202).
In some embodiments, the camera 11 may record and capture vehicle information passing through the intersection via the monitoring system, and send the collected vehicle image data to the server 12 of the monitoring system, and the server 12 may store the vehicle image data in the transportation vehicle database. The vehicle image data includes image data and/or video data of the vehicle.
2) The server 12 extracts the features of the vehicle image data of the to-be-detected license plate vehicle to obtain vehicle feature information (204).
In some embodiments, the vehicle characteristic information may be, but is not limited to, license plate information, body color, license plate number, emblem pattern, vehicle brand, vehicle type, annual check mark, sun visor, pendant, etc. of the vehicle.
In some embodiments, the feature vector extraction is performed on the image by a Local Binary Patterns (LBP) algorithm, a Histogram of Oriented Gradient (HOG) algorithm, a Haar-like algorithm, a Convolutional Neural Network (CNN), and the like to obtain the vehicle feature information, but is not limited thereto.
In some embodiments, the vehicle characteristic information includes, but is not limited to, license plate number, body color, vehicle brand, vehicle type, and the like. And (3) performing feature extraction on the vehicle image data of the vehicle to be detected by adopting a Convolutional Neural Network (CNN) algorithm to obtain vehicle feature information. For example, the main steps include feature extraction, feature mapping, sub-sampling. The specific content is that input image data is convoluted through N trainable filters and an applicable bias, M middle layers are generated after convolution, wherein three feature maps are generated on a C1 layer, then a plurality of pixels of each group in the feature maps are summed, weighted values are added with the bias, and feature maps of three S2 layers are obtained through a Sigmoid function. These maps are filtered to obtain the layer C3. This hierarchy, again, as with S2, results in S4. Finally, the pixel values are rasterized and connected into a vector to be input into a conventional neural network, and output vehicle characteristic information is obtained.
It can be understood that the vehicle feature information in the extracted vehicle image provides sufficient vehicle information for subsequent sufficient determination of the fake-licensed vehicle detection behavior.
3) The server 12 determines the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data of the vehicle with the license plate to be detected (206).
In some embodiments, the server 12 determines vehicle characteristic information from the vehicle imagery data. The vehicle image data is collected through the camera 11, and the collected vehicle image data and the collection time thereof are transmitted to the server 12 for storage and further processing.
Further, in some embodiments, the vehicle image data includes acquisition time information and vehicle characteristic information of the vehicle image data.
The vehicle driving track is composed of the acquisition time of vehicle image data and vehicle position information within a period of time. The vehicle driving track may include the collection time information of the vehicle image data and the vehicle position information, for example, the vehicle driving track is: (t1, a) → (t2, a) → (t3, a) → (t4, B). t1, t2, t3 and t4 represent the time of vehicle image data acquisition, and A and B represent the area position of the vehicle, which can be latitude and longitude coordinates, and can also be the name of a place corresponding to the latitude and longitude coordinates. The dwell time at point a is t3-t1, whereby we can record the internal dwell time of each spot and each different spot not being dwell so that there are no consecutive identical spots in the track. The internal dwell time of a site refers to the maximum time interval of the periodic detection.
In some embodiments, for a vehicle with a license plate to be detected, the shooting position and the shooting time information of the vehicle are acquired from a traffic vehicle database, and the more the time point and the position information of the target vehicle are acquired, the more accurate the behavior track of the vehicle is. Specifically, taking the behavior track data of the license plate number detected by the camera 11 as the Yu A13818 as an example, as shown in the following list, the camera 11 collects track information of the license plate number Yu A13818 from 7 points 30 in the morning to 7 points in the evening, wherein 11 time points and position information of the vehicle are obtained from the time point 30 in the morning to 7 points in the evening.
And carrying out anomaly detection on the behavior track to obtain an anomaly detection result. And judging the abnormal detection result according to the target adaptation probability, wherein if the target adaptation probability is greater than the matching threshold, the vehicle is a suspected fake-licensed vehicle. In particular, in someIn the examples, PiRepresenting the behaviour pattern of the vehicle, QjRepresenting a noisy environment, e.g., a vehicle missing a time. PiAnd QjDetermines the vehicle track TijWhen no deck action exists, the actual trajectory will be a single trajectory; when there is a fake-card action, the actual trajectory will be a superposition of multiple single trajectories, i.e. T ═ Σ(ij)TijAnd setting the superposition probability of the single track and the plurality of single tracks in the range of 0 to 1, namely the target adaptation probability, and if the target adaptation probability is greater than the matching threshold, determining that the vehicle is a suspected fake-licensed vehicle.
4) When the server 12 detects that at least two vehicle tracks exist at the same time in the license plate vehicle to be detected, the license plate vehicle to be detected is determined to be a suspected fake-plate vehicle (208).
When the server 12 judges that the running tracks of other vehicles in the same license plate vehicle track database coincide with the running track of the vehicle of the license plate to be detected at the same time, the server determines that the vehicle of the license plate vehicle to be detected and the vehicle coinciding with the vehicle track of the license plate vehicle to be detected are suspected fake-licensed vehicles.
In some embodiments, the vehicle driving track includes a track sequence composed of time and place, the time is the collection time of the vehicle image data, and the place is the position of the license plate vehicle to be detected at the collection time. The location may be latitude and longitude coordinates or a range of areas. The accuracy of coordinates is high, but too fine positioning may be unfavorable for comparison (repeated redundancy) between tracks, and a camera is a direct method for acquiring track data at present and covers coordinate information. Therefore, the vehicle image data and the vehicle position information of the vehicle with the license plate to be detected are acquired once every preset time.
In some embodiments, a behavior track of a vehicle of a license plate vehicle to be detected is determined by acquiring a large amount of image data and vehicle position information of a traffic monitoring camera, the behavior track is a two-dimensional space coordinate system corresponding to time and position, and the time and the position are respectively the time shot by the traffic monitoring camera and the position of the traffic monitoring camera. The internal dwell time of a site refers to the maximum time interval for continuous snapshots at that point, for example: the behavior trajectory (t1, a) → (t2, a) → (t3, a) → (t4, B), it being understood that t1, t2, t3, t4 are time information of a certain vehicle, and a, B are position information corresponding to a certain vehicle. The dwell time at point a is t3-t1, whereby we can record the internal dwell time of each location and the corresponding number of snapshots, so that no consecutive identical locations are present in the trajectory. The calculation of the internal stay time has high requirements on the snapshot, and the probability of the internal stay time in most places is 0.
However, the case of multiple cameras one by one causes more noise, and there is no direct advantage in calculating the coordinates in distance. The area is a self-defined range, and the track can be read more abundantly by matching with a self-defined label. The above recommendations are used to record locations in a mixed manner of coordinates (point area) and area.
If two vehicles with the same license plate have two longitude and latitude coordinates or area positions at the same time, determining that the two vehicles or a plurality of vehicles with the same license plate are suspected fake-licensed vehicles. Specifically, the method comprises the following steps:
the vehicle driving track is a mapping relation table between acquisition time information and vehicle position information of vehicle image data, and when at least two vehicle tracks of the vehicle with the license plate to be detected exist at the same time, the vehicle with the license plate to be detected is determined to be a suspected fake-licensed vehicle, and the method comprises the following steps:
inquiring a mapping relation table;
and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle.
The vehicle characteristic information also includes vehicle attribute information, which is information such as a vehicle body color characteristic, a vehicle body height, and the like, and vehicle occupant information, which includes facial characteristics of a driver, a passenger person, and the like.
When the fact that at least two vehicle tracks of the license plate vehicle to be detected exist at the same time is detected, the license plate vehicle to be detected is determined to be a suspected fake-licensed vehicle, whether vehicle attribute information and vehicle occupant information in vehicle image data of the at least two vehicle tracks existing at the same time are the same is further judged, and the license plate vehicle to be detected is further determined to be the suspected fake-licensed vehicle under the condition that the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data are different.
The license plate information is the unique identification characteristics of the vehicles, and for a suspected fake-licensed vehicle, after the license plates of two vehicles are detected to be consistent, the vehicle characteristics such as vehicle marks, vehicle body colors, vehicle types and the like become the main identification characteristics of the vehicle. Further, if the license plate, the model, the logo, the body color and the like are almost the same, the driver and the passengers of the two vehicles of the suspected fake-licensed vehicle are judged to be consistent or not by obtaining the appearance and the facial features of the driver of the suspected fake-licensed vehicle. It can be understood that suspected fake-licensed vehicles are locked through the license plate character features, and the vehicle marks, the vehicle body color features, the vehicle facial features, the appearances, the facial features and the like of drivers and passengers of the suspected fake-licensed vehicles are further compared, so that whether the fake-licensed vehicles exist is finally judged.
FIG. 3 illustrates a block diagram of a fake-licensed vehicle detection device 300, according to some embodiments of the present application. As shown in fig. 3, specifically, the method includes:
and the data acquisition module (302) is used for acquiring vehicle image data and vehicle position information of a vehicle with a license plate to be detected.
And the feature extraction module (304) is used for performing feature extraction on the vehicle image data of the vehicle with the license plate to be detected to obtain vehicle feature information.
And the behavior track determining module (306) is used for determining the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data.
And the fake-licensed vehicle determining module (308) is used for determining that the fake-licensed vehicle to be detected is a suspected fake-licensed vehicle if at least two vehicle tracks of the fake-licensed vehicle to be detected exist at the same time.
In some embodiments, the fake-licensed vehicle determination module is further configured to query a mapping table;
and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle. The behavior track determining module is used for determining the vehicle position information as longitude and latitude coordinates or a position area of the vehicle.
In some embodiments, the fake-licensed vehicle determining module is further configured to determine whether vehicle attribute information or vehicle occupant information in the vehicle image data in which at least two vehicle trajectories exist at the same time is the same, and further determine that the license plate vehicle to be detected is a fake-licensed vehicle if the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data is different.
It can be understood that the fake-licensed vehicle detecting device 300 shown in fig. 3 corresponds to the fake-licensed vehicle detecting method provided by the present application, and the technical details in the above detailed description about the fake-licensed vehicle detecting method provided by the present application are still applicable to the fake-licensed vehicle detecting device 300 shown in fig. 3, and the detailed description is referred to above and is not repeated herein.
Fig. 4 is a block diagram illustrating a system 400 according to some embodiments of the present application. FIG. 4 schematically illustrates an example system 400 in accordance with various embodiments. In some embodiments, system 400 may include one or more processors 404, system control logic 408 coupled to at least one of processors 404, system memory 412 coupled to system control logic 408, non-volatile memory (NVM)416 coupled to system control logic 408, and a network interface 420 coupled to system control logic 408.
In some embodiments, processor 404 may include one or more single-core or multi-core processors. In some embodiments, the processor 404 may include any combination of general-purpose processors and special-purpose processors (e.g., graphics processors, application processors, baseband processors, etc.).
In some embodiments, system control logic 408 may include any suitable interface controllers to provide any suitable interface to at least one of processors 404 and/or any suitable device or component in communication with system control logic 408.
In some embodiments, system control logic 408 may include one or more memory controllers to provide an interface to system memory 412. System memory 412 may be used to load and store data and/or instructions. The memory 412 of the system 400 may include any suitable volatile memory, such as suitable Dynamic Random Access Memory (DRAM), in some embodiments.
NVM/memory 416 may include one or more tangible, non-transitory computer-readable media for storing data and/or instructions. In some embodiments, the NVM/memory 416 may include any suitable non-volatile memory such as flash memory and/or any suitable non-volatile storage device, such as at least one of a HDD (Hard Disk Drive), CD (Compact Disc) Drive, DVD (Digital Versatile Disc) Drive.
The NVM/memory 416 may comprise a portion of the storage resources on the device on which the system 400 is installed, or it may be accessible by, but not necessarily a part of, the device. For example, NVM/storage 416 may be accessed over a network via network interface 420.
In particular, system memory 412 and NVM/storage 416 may each include: a temporary copy and a permanent copy of the instructions 424. The instructions 424 may include: instructions that when executed by at least one of the processors 404 cause the system 400 to implement the method shown in fig. 3-4. In some embodiments, the instructions 424, hardware, firmware, and/or software components thereof may additionally/alternatively be disposed in the system control logic 408, the network interface 420, and/or the processor 404.
Network interface 420 may include a transceiver to provide a radio interface for system 400 to communicate with any other suitable device (e.g., front end module, antenna, etc.) over one or more networks. In some embodiments, network interface 420 may be integrated with other components of system 400. For example, network interface 420 may be integrated with at least one of processor 404, system memory 412, NVM/storage 416, and a firmware device (not shown) having instructions that, when executed by at least one of processors 404, system 400 implements the fake-licensed vehicle detection method as shown in fig. 2.
Network interface 420 may further include any suitable hardware and/or firmware to provide a multiple-input multiple-output radio interface. For example, network interface 420 may be a network adapter, a wireless network adapter, a telephone modem, and/or a wireless modem.
In one embodiment, at least one of the processors 404 may be packaged together with logic for one or more controllers of system control logic 408 to form a System In Package (SiP). In one embodiment, at least one of processors 404 may be integrated on the same die with logic for one or more controllers of system control logic 408 to form a system on a chip (SoC).
The system 400 may further include: input/output (I/O) devices 432. I/O device 432 may include a user interface to enable a user to interact with system 400; the design of the peripheral component interface enables peripheral components to also interact with the system 400. In some embodiments, the system 400 further comprises a sensor for determining at least one of environmental conditions and location information associated with the system 400.
Fig. 5 shows a block diagram of a SoC (System on Chip) 500, according to an embodiment of the present application. In fig. 5, similar components have the same reference numerals. In addition, the dashed box is an optional feature of more advanced socs. In fig. 5, SoC 500 includes: an interconnect unit 550 coupled to the application processor 510; a system agent unit 570; a bus controller unit 580; an integrated memory controller unit 540; a set or one or more coprocessors 520 which may include integrated graphics logic, an image processor, an audio processor, and a video processor; a Static Random Access Memory (SRAM) unit 530; a Direct Memory Access (DMA) unit 560. In one embodiment, coprocessor 520 comprises a special-purpose processor, such as, for example, a network or communication processor, compression engine, GPU, high-throughput MIC processor, embedded processor, or the like.
Embodiments of the mechanisms disclosed herein may be implemented in hardware, software, firmware, or a combination of these implementations. Embodiments of the application may be implemented as computer programs or program code executing on programmable systems comprising at least one processor, a storage system (including volatile and non-volatile memory and/or storage elements), at least one input device, and at least one output device.
Program code may be applied to input instructions to perform the functions described herein and generate output information. The output information may be applied to one or more output devices in a known manner. For purposes of this application, a processing system includes any system having a processor such as, for example, a Digital Signal Processor (DSP), a microcontroller, an Application Specific Integrated Circuit (ASIC), or a microprocessor.
The program code may be implemented in a high level procedural or object oriented programming language to communicate with a processing system. The program code can also be implemented in assembly or machine language, if desired. Indeed, the mechanisms described in this application are not limited in scope to any particular programming language. In any case, the language may be a compiled or interpreted language.
In some cases, the disclosed embodiments may be implemented in hardware, firmware, software, or any combination thereof. The disclosed embodiments may also be implemented as instructions carried by or stored on one or more transitory or non-transitory machine-readable (e.g., computer-readable) storage media, which may be read and executed by one or more processors. For example, the instructions may be distributed via a network or via other computer readable media. Thus, a machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computer), including, but not limited to, floppy diskettes, optical disks, read-only memories (CD-ROMs), magneto-optical disks, read-only memories (ROMs), Random Access Memories (RAMs), erasable programmable read-only memories (EPROMs), electrically erasable programmable read-only memories (EEPROMs), magnetic or optical cards, flash memory, or tangible machine-readable memories for transmitting information using the Internet in the form of electrical, optical, acoustical or other propagated signals, e.g., carrier waves, infrared digital signals, etc.). Thus, a machine-readable medium includes any type of machine-readable medium suitable for storing or transmitting electronic instructions or information in a form readable by a machine (e.g., a computer).
In the drawings, some features of the structures or methods may be shown in a particular arrangement and/or order. However, it is to be understood that such specific arrangement and/or ordering may not be required. Rather, in some embodiments, the features may be arranged in a manner and/or order different from that shown in the illustrative figures. In addition, the inclusion of a structural or methodical feature in a particular figure is not meant to imply that such feature is required in all embodiments, and in some embodiments, may not be included or may be combined with other features.
It should be noted that, in the embodiments of the apparatuses in the present application, each unit/module is a logical unit/module, and physically, one logical unit/module may be one physical unit/module, or may be a part of one physical unit/module, and may also be implemented by a combination of multiple physical units/modules, where the physical implementation manner of the logical unit/module itself is not the most important, and the combination of the functions implemented by the logical unit/module is the key to solve the technical problem provided by the present application. Furthermore, in order to highlight the innovative part of the present application, the above-mentioned device embodiments of the present application do not introduce units/modules which are not so closely related to solve the technical problems presented in the present application, which does not indicate that no other units/modules exist in the above-mentioned device embodiments.
It is noted that, in the examples and descriptions of this patent, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, 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, the use of the verb "comprise a" to define an element does not exclude the presence of another, same element in a process, method, article, or apparatus that comprises the element.
While the present application has been shown and described with reference to certain preferred embodiments thereof, it will be understood by those of ordinary skill in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present application.
Claims (10)
1. A method of fake-licensed vehicle detection, the method comprising:
acquiring vehicle image data of a vehicle with a license plate to be detected, and acquiring vehicle position information of the vehicle with the license plate to be detected;
carrying out feature extraction on the vehicle image data of the vehicle with the license plate to be detected to obtain vehicle feature information;
determining a vehicle running track of a vehicle with a license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data;
and when detecting that the license plate vehicle to be detected has at least two vehicle tracks at the same time, determining that the license plate vehicle to be detected is a suspected fake-licensed vehicle.
2. The method for detecting a fake-licensed vehicle according to claim 1, wherein the vehicle driving trajectory is a mapping relationship table between the acquisition time information of the vehicle image data and the vehicle position information, and when it is detected that at least two vehicle trajectories exist at the same time for the license plate vehicle to be detected, it is determined that the detected license plate vehicle is a suspected fake-licensed vehicle, including:
inquiring the mapping relation table;
and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle.
3. The fake-licensed vehicle detecting method according to claim 1, wherein the vehicle location information is longitude and latitude coordinates or a location area of the vehicle.
4. The fake-licensed vehicle detection method of claim 2, further comprising: and judging whether the vehicle attribute information or the vehicle occupant information in the vehicle image data with at least two vehicle tracks at the same time is the same, and further determining that the license plate vehicle to be detected is a fake plate vehicle under the condition that the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data is different.
5. A fake-licensed vehicle detecting device, the device comprising:
the data acquisition module is used for acquiring vehicle image data and vehicle position information of a vehicle with a license plate to be detected;
the characteristic extraction module is used for extracting the characteristics of the vehicle image data of the vehicle with the license plate to be detected to obtain vehicle characteristic information;
the behavior track determining module is used for determining the vehicle running track of the vehicle with the license plate to be detected according to the vehicle characteristic information, the vehicle position information and the acquisition time information of the vehicle image data;
and the fake-licensed vehicle determining module is used for determining that the fake-licensed vehicle to be detected is a suspected fake-licensed vehicle if at least two vehicle tracks of the fake-licensed vehicle to be detected exist at the same time.
6. The fake-licensed vehicle detecting apparatus of claim 5, wherein the fake-licensed vehicle determining module comprises:
inquiring a mapping relation table;
and if the mapping relation table has two or more tracks corresponding to the same time period, determining the license plate vehicle to be detected as a suspected fake-license plate vehicle.
7. The fake-licensed vehicle detecting device of claim 5, wherein the behavior trace determining module is configured to determine the vehicle location information as longitude and latitude coordinates or a location area of the vehicle.
8. The fake-licensed vehicle detecting device according to claim 6, wherein the fake-licensed vehicle determining module is further configured to determine whether vehicle attribute information or vehicle occupant information in the vehicle image data where at least two vehicle trajectories exist at the same time is the same, and further determine that the license plate vehicle to be detected is a suspected fake-licensed vehicle if the vehicle attribute information or the vehicle occupant information corresponding to the two vehicle image data is different.
9. A machine-readable medium having stored thereon instructions which, when executed on a machine, cause the machine to perform the method of detecting a fake-licensed vehicle of any one of claims 1 to 4.
10. A system, comprising:
a memory for storing instructions for execution by one or more processors of the system, an
A processor, being one of the processors of the system, for performing the fake-licensed vehicle detection method of any one of claims 1 to 4.
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Application publication date: 20201201 |