CN113129597B - Method and device for identifying illegal vehicles on motor vehicle lane - Google Patents
Method and device for identifying illegal vehicles on motor vehicle lane Download PDFInfo
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- CN113129597B CN113129597B CN201911405630.6A CN201911405630A CN113129597B CN 113129597 B CN113129597 B CN 113129597B CN 201911405630 A CN201911405630 A CN 201911405630A CN 113129597 B CN113129597 B CN 113129597B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
- G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/50—Context or environment of the image
- G06V20/52—Surveillance or monitoring of activities, e.g. for recognising suspicious objects
Abstract
The application is suitable for the technical field of image processing, and provides a method for identifying illegal vehicles on a motor vehicle lane, which comprises the following steps: the method comprises the steps of obtaining and identifying image data of a preset identification area, obtaining an identification result of whether a two-wheel vehicle is included in the image data, analyzing and obtaining vehicle information of the two-wheel vehicle if the image data includes the two-wheel vehicle, detecting whether the vehicle information includes a preset legal identification, and generating alarm information according to the vehicle information if the vehicle information does not include the preset legal identification. The method and the device have the advantages that whether the vehicle in the image data is the two-wheel vehicle is determined by acquiring and identifying the image data of the preset identification area, the vehicle information of the vehicle is acquired when the image data comprises the two-wheel vehicle, and when the preset legal identification is not included in the vehicle information, the warning information is generated according to the vehicle information, so that the accuracy and the reliability of identifying whether the two-wheel vehicle exists in the motor vehicle lane are improved, and the efficiency of acquiring the vehicle information of the illegal two-wheel vehicle is improved.
Description
Technical Field
The application belongs to the technical field of image processing, and particularly relates to a method and a device for identifying illegal vehicles on a motor vehicle lane.
Background
Because the two-wheeled vehicle and the motor vehicle have different running speeds and safety degrees, the motor vehicle lane and the non-motor vehicle lane are usually distinguished in the road planning process, and the occurrence of unexpected safety accidents is avoided.
Therefore, in order to standardize the road traffic safety regulations, it is necessary to identify whether there are illegal two-wheeled vehicles on the motorway.
The existing identification method is difficult to identify whether the two-wheeled vehicle of the motor lane is a legal vehicle, so that the road traffic safety regulation is difficult to standardize, and the accuracy rate of vehicle illegal early warning is reduced.
Disclosure of Invention
The embodiment of the application provides a method and a device for identifying illegal vehicles on a motor vehicle lane, which can solve the problem that in the prior art, the driving information and the vehicle information of an illegal two-wheel vehicle are difficult to mark rapidly, so that the road traffic safety regulation is difficult to standardize.
In a first aspect, an embodiment of the present application provides a method for identifying an illegal vehicle on a motor vehicle lane, including:
acquiring image data of a preset identification area;
identifying the image data, and determining whether the image data comprises an identification result of the two-wheel vehicle according to the state data of the target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle;
if the identification result is that the image data comprises a two-wheel vehicle, vehicle information of the two-wheel vehicle is obtained;
detecting whether the vehicle information comprises a preset legal identifier or not;
and if the vehicle information does not comprise a preset legal identifier, generating alarm information according to the vehicle information.
In a second aspect, an embodiment of the present application provides an apparatus for identifying a vehicle with an illegal motor vehicle lane, including:
the acquisition module is used for acquiring image data of a preset identification area;
the identification module is used for identifying the image data and determining whether the image data comprises an identification result of the two-wheel vehicle according to the state data of the target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle;
the analysis module is used for acquiring vehicle information of the two-wheel vehicle if the identification result is that the image data comprises the two-wheel vehicle;
the detection module is used for detecting whether the vehicle information comprises a preset legal identification;
and the generating module is used for generating alarm information according to the vehicle information if the vehicle information does not comprise a preset legal identifier.
In a third aspect, an embodiment of the present application provides a terminal device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor, when executing the computer program, implements the method for identifying a vehicle with a motor lane violation according to any one of the first aspect.
In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program, and the computer program, when executed by a processor, implements the method for identifying a vehicle with a motor lane violation according to any of the first aspect.
In a fifth aspect, the present application provides a computer program product, when the computer program product runs on a terminal device, the terminal device is caused to execute the method for identifying a vehicle with a motor lane violation according to any one of the first aspect.
It is to be understood that, for the beneficial effects of the second aspect to the fifth aspect, reference may be made to the relevant description in the first aspect, and details are not described herein again.
According to the embodiment of the application, the image data of the preset identification area is obtained, the image data is identified, whether the vehicle in the image data is a two-wheel vehicle or not is determined, the vehicle information of the vehicle is obtained when the image data comprises the two-wheel vehicle, and when the vehicle information does not comprise the preset legal identification, the alarm information is generated according to the vehicle information, so that the accuracy and the reliability of identifying whether the two-wheel vehicle exists in the motor vehicle lane or not are improved, and the efficiency of obtaining the vehicle information of the illegal two-wheel vehicle is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a method for identifying an illegal vehicle on a motor vehicle lane according to an embodiment of the present application;
FIG. 2 is a schematic view of an application scenario of a method for identifying an illegal vehicle based on a motor vehicle lane according to an embodiment of the present application;
FIG. 3 is a schematic diagram of an application scenario of a method for recognizing a vehicle based on a motor vehicle lane violation according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of a vehicle identification device for vehicle lane violation according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a terminal device according to another embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
It should also be understood that the term "and/or" as used in this specification and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.
As used in this specification and the appended claims, the term "if" may be interpreted contextually as "when", "upon" or "in response to" determining "or" in response to detecting ". Similarly, the phrase "if it is determined" or "if a [ described condition or event ] is detected" may be interpreted contextually to mean "upon determining" or "in response to determining" or "upon detecting [ described condition or event ]" or "in response to detecting [ described condition or event ]".
Furthermore, in the description of the present application and the appended claims, the terms "first," "second," "third," and the like are used for distinguishing between descriptions and not necessarily for describing or implying relative importance.
Reference throughout this specification to "one embodiment" or "some embodiments," or the like, means that a particular feature, structure, or characteristic described in connection with the embodiment is included in one or more embodiments of the present application. Thus, appearances of the phrases "in one embodiment," "in some embodiments," "in other embodiments," or the like, in various places throughout this specification are not necessarily all referring to the same embodiment, but rather "one or more but not all embodiments" unless specifically stated otherwise. The terms "comprising," "including," "having," and variations thereof mean "including, but not limited to," unless expressly specified otherwise.
The method for recognizing the illegal vehicle in the motor vehicle lane can be applied to Mobile phones, tablet computers, wearable devices, vehicle-mounted devices, Augmented Reality (AR)/Virtual Reality (VR) devices, notebook computers, Ultra-Mobile Personal computers (UMPC), netbooks, Personal Digital Assistants (PDA) and other terminal devices, and the embodiment of the application does not limit the specific types of the terminal devices.
Fig. 1 shows a schematic flow chart of a method for identifying an illegal vehicle in a motor vehicle lane provided by the present application, which can be applied to any one of the terminal devices described above by way of example and not limitation.
S101, acquiring image data of a preset identification area;
in specific application, the camera which is preset at the intersection of the motor vehicle lane and used for illegal recognition is used for shooting image data in a corresponding preset recognition area. The image data includes, but is not limited to, pictures or videos.
It should be noted that the number of the preset identification regions may be determined according to actual situations, for example, N corresponding preset identification regions may be set at N motor vehicle road intersections, or N × 2 corresponding preset identification regions may be set.
S102, identifying the image data, and determining whether the image data comprises an identification result of the two-wheel vehicle according to the state data of the target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle;
in specific application, the image data is identified according to a preset algorithm, and more than one of the number of vehicles, the number of target objects in any vehicle and the state data of any target object in the image data is identified; and analyzing the more than one identified data to determine whether the image data comprises the identification result of the two-wheel vehicle. The preset algorithm includes, but is not limited to, at least one of an image analysis algorithm, a face recognition algorithm, and a human body recognition algorithm.
For example, if the actual distance between a plurality of target objects in a certain vehicle is greater than a preset distance threshold, the vehicle is determined to be a motor vehicle.
S103, if the image data comprise the two-wheel vehicle, vehicle information of the two-wheel vehicle is obtained;
in a specific application, if the image data of any preset area includes a two-wheel vehicle, the vehicle information of the two-wheel vehicle is identified and obtained. The vehicle information includes but is not limited to at least one of preset legal identification, license plate number, vehicle owner identity information, vehicle model, vehicle number of people checking and vehicle owner illegal information.
S104, detecting whether the vehicle information comprises a preset legal identification;
in a specific application, the preset legal mark is a preset mark that a vehicle can legally run to any road. The preset legal identifier can be specifically set according to actual conditions, for example, vehicles such as a police motor vehicle, a police motorcycle, a police electric vehicle, any ambulance, a fire fighting truck and the like are set to be provided with the corresponding preset legal identifiers. The legal marks of the police motor vehicle, the police motorcycle and the police electric vehicle are that the license plate number has a prefix of a police character or the box body of the vehicle is marked with police content.
And S105, if the vehicle information does not include the preset legal identification, generating alarm information according to the vehicle information.
In a specific application, if the vehicle information of the two-wheel vehicle does not include the preset legal identification, the two-wheel vehicle is judged to be the illegally-driven two-wheel vehicle, the warning information is generated according to the vehicle information of the two-wheel vehicle, and corresponding processing (such as deducting a driving license of a vehicle owner, carrying out fine processing on the vehicle owner and the like) is carried out on the illegally-driven two-wheel vehicle according to the road traffic laws and regulations.
The warning information may include, but is not limited to, a picture or a video of the illegally traveling two-wheeled vehicle, an illegal event or an illegal place, and the like, or further include the number of illegal events, illegal penalty information, and the like of the driver of the illegally traveling two-wheeled vehicle.
In one embodiment, before the step S101, the method includes:
and setting a corresponding preset identification area according to the actual data of the motor vehicle lane.
In specific application, the actual data (such as width) of different motor vehicle lanes are different, and a corresponding preset identification area is set according to the actual data of each motor vehicle lane;
or acquiring the actual data of all the motor vehicle lanes in the friction-forbidden region, and setting a preset identification region corresponding to the friction-forbidden region.
For example, if a certain road is a one-way road and the width of the one-way road is 8M, the corresponding preset identification area is set as an area with the actual width of 8M;
for example, if the city a stipulates that the area a prohibits motorcycle driving, the actual data of all the lanes in the area a is acquired, and a preset identification area corresponding to the area a is set.
As shown in fig. 2, a schematic diagram of an application scenario for a method for identifying an illegal vehicle based on a motor vehicle lane is provided.
In fig. 2, a camera for performing illegal recognition is disposed at an intersection of a gray motor vehicle lane, and a preset recognition area corresponding to image data captured by the camera is the gray motor vehicle lane.
If a road is three roads, the width of the road is 30M, and the road intersection can not turn left or right and can only go straight, the corresponding preset identification area is determined to be a straight area with the actual width equal to 30M and does not include left-turning and right-turning areas of the road.
The corresponding preset identification areas are set according to the actual data of different motor vehicle lanes, the image data of the motor vehicle lane corresponding to the preset identification areas can be accurately acquired, the image data of the motor vehicle lane is analyzed, the problem that the calculation analysis data volume is large due to the fact that the area setting range is too large is solved, and the efficiency and the accuracy of image data analysis are guaranteed.
In one embodiment, the step S102 includes:
s1021, identifying the image data and obtaining state data of a target object in the image data;
s1022, judging whether the state data meet preset conditions or not;
and S1023, if the state data meet the preset conditions, judging that the identification result is that the image data comprises the two-wheel vehicle.
In specific application, the image data is identified through a preset algorithm to obtain state data of a target object in the image data, whether the state data meets a preset condition or not is judged, and whether a two-wheel vehicle exists in the image data or not is judged according to whether the state data meets the preset condition or not: if the state data do not meet the preset conditions, judging that the identification result is that the two-wheel vehicle is not included in the image data;
and if the state data meet the preset conditions, judging that the identification result is that the image data comprises the two-wheel vehicle.
The preset condition refers to a plurality of thresholds corresponding to the state data of the plurality of target objects, and different thresholds can be specifically set according to different types of the state data.
For example, the preset condition is set to determine whether the actual distance between the target objects in the vehicle is smaller than a preset distance threshold, and if the actual distance between two target objects is smaller than the preset distance threshold, the vehicle in which the two target objects are located is a two-wheeled vehicle.
The state data of the target object is obtained through analysis and identification, and the identification result of whether the vehicle taken by the target object is the two-wheel vehicle is obtained through comparison between the state data and the corresponding preset conditions, so that the purpose of scientifically analyzing the vehicle type in the motor vehicle lane is achieved, the safety degree of the target object can be detected through the state data of the target object, and the safety of a user of the motor vehicle or the two-wheel vehicle is guaranteed.
In one embodiment, the status data includes, but is not limited to, sharpness, clothing data, integrity of image feature values, actual distances between multiple target objects within any vehicle, and positional relationships of target objects to the vehicle.
In one embodiment, the step S1022 includes:
s10221, judging whether the definition of the target object is greater than a preset definition threshold value; and the combination of (a) and (b),
s10222, judging whether the clothing data of the target object comprises a safety helmet; and the combination of (a) and (b),
s10223, judging whether the integrity of the image characteristic value of the target object is larger than an integrity threshold value of a preset image characteristic value; and the combination of (a) and (b),
s10224, judging whether the actual distances among a plurality of target objects in the vehicle are smaller than a preset distance threshold value; and the combination of (a) and (b),
s10225, judging whether the relation between the target object and the vehicle conforms to at least one of preset relations.
In a specific application, since the target object in a conventional motor vehicle may have low visibility due to the windshield, it is possible to determine whether the target object is a driver or a passenger of the two-wheeled vehicle according to the determination result of whether the visibility of the target object is greater than the preset visibility threshold. For example, if the definition of the target object is less than or equal to the preset definition threshold, it is determined that the target object is a person as a driver of a motor vehicle or a passenger of the motor vehicle.
The preset definition threshold value can be specifically set according to actual conditions. For example, the sharpness threshold is set to be an average value of the sharpness of the regions other than the target object face region in the vehicle in the image data.
Since it is necessary to wear a safety helmet when driving a motorcycle such as a motorcycle or an electric vehicle according to traffic safety regulations, it is possible to determine whether or not the target object is a driver or a passenger of the motorcycle by determining whether or not the target object includes the safety helmet.
Because most of the body of a target object in a conventional motor vehicle is shielded by a vehicle body, whether the target object is a two-wheel vehicle driver or a passenger can be judged by calculating the integrity of the image characteristic value of the human body of the target object and judging whether the integrity of the image characteristic value of the target object is greater than the integrity threshold of the preset image characteristic value.
The integrity threshold of the preset image characteristic value can be specifically set according to actual conditions. For example, if the integrity of the image feature value of the target object is less than or equal to a preset threshold of the integrity of the image feature value, it may be determined that the target object is a driver or a passenger of the motor vehicle.
And obtaining that the vehicle body of the motor vehicle can block at least 2/3 contours of the human body of the target object according to experiments, wherein the preset integrity threshold is 1/3 of the human body area integrity of the target object.
For example: the human body area of a normal target object is set to be 1.5m × 0.5m-1.9m × 0.5m, and if the ratio of data to actual data in an image is 1:100, that is, 1cm in the image is 1m in reality, the threshold value of the integrity of the preset image characteristic value is 1.5cm × 0.5cm × 1/3-1.9cm × 0.5cm × 1/3.
And if the integrity of the image characteristic value of the target object is less than or equal to the integrity threshold of the preset image characteristic value, namely the integrity of the image characteristic value of the target object is less than or equal to 1.5cm 0.5cm 1/3-1.9cm 0.5cm 1/3, judging that the target object is a motor vehicle driver or a passenger.
Since there is a certain distance between the driving seat and the passenger seat in the motor vehicle, and the distance between the driver and the passenger is relatively short after most of the non-motor vehicles carry people, if it is detected that the actual distance between any two or more target objects in any vehicle is smaller than the preset distance threshold value, the vehicle on which the two or more target objects are located can be determined to be a two-wheeled vehicle.
The preset distance threshold value can be specifically set according to actual conditions. For example: and setting the preset distance threshold value to be 50cm when the actual distance between the driving position and the auxiliary driving position is 50 cm-60 cm.
Setting the ratio of data to actual data in the image as 1:100, i.e. 1cm in the image is 1m in practice. If the distance between any two or more target objects in any vehicle in the image data is less than 0.5cm, that is, the actual distance between the two or more target objects is less than 50cm, the vehicle on which the two or more target objects can be seated is a two-wheeled vehicle.
The preset relationship can be specifically set according to actual conditions, and in practical application, the target object and the license plate number of the conventional motor vehicle in the image data are generally on the same straight line. The target object and the license plate number in the two-wheeled vehicle are generally a diagonal line. For example, the preset relationship is set such that the positional relationship between the target object and the license plate number is in a diagonal line.
Therefore, if the positional relationship between the target object and the license plate number is a diagonal line, it is determined that the vehicle is a two-wheeled vehicle.
As shown in fig. 3, a schematic view of an application scenario of the method for recognizing illegal vehicles based on motor vehicle lanes is provided.
In fig. 3, it can be recognized that the target object wears the helmet and the human body area integrity of the target object is greater than 1/3, and it can be determined that the vehicle on which the target object is mounted is a two-wheeled vehicle.
According to the state data of the target object in different states in actual life application, preset conditions corresponding to different types and numerical values are set, and whether the vehicle is a two-wheel vehicle or not can be analyzed and identified in an all-round mode through judgment of the state data of various target objects, so that the efficiency of identifying the two-wheel vehicle is improved.
In one embodiment, if the image data includes any vehicle with articles such as sunshade umbrella, windproof gloves, etc., the vehicle is determined to be a two-wheel vehicle.
In one embodiment, before S10224, the method includes:
acquiring the length of any reference object in the image data and the actual length of the reference object;
calculating the ratio of the length of the reference object in the image data to the actual length;
acquiring virtual distances between a plurality of target objects in each vehicle in the image data;
and calculating according to the proportion and the virtual distance between the target objects in each vehicle in the image data to obtain the actual distance between the target objects in each vehicle.
In a specific application, the ratio of the length of the reference object in the image data to the actual length can be calculated and obtained by firstly calculating the length of the reference object in the image data and the actual length of the reference object. For example, if the actual length of any reference object is 1m and the length thereof in the image data is 1cm, the ratio of the length of the reference object in the image data to the actual length is 1: 100.
Then, the virtual distance between the target objects in each vehicle in the image data is obtained, the virtual distance between the target objects in each vehicle in the image data and the ratio are calculated, and the actual distance between the target objects in each vehicle can be obtained.
The corresponding proportion is obtained by calculating the proportion between the length of the reference object in the image data and the actual length, the distances among the plurality of objects are calculated according to the corresponding proportion, and the position relation among the plurality of objects is obtained accordingly, so that the judgment condition of whether the vehicle is a two-wheel vehicle is obtained, and the accuracy of the judgment result is improved.
In one embodiment, after the step S104, the method includes:
and if the vehicle information comprises a preset legal identification, judging that the two-wheel vehicle is a legal vehicle, and returning to execute the acquisition of the image data of the preset identification area.
In a specific application, if the vehicle information of one two-wheeled vehicle includes a preset legal identification, the two-wheeled vehicle is judged to be a legal vehicle, namely the two-wheeled vehicle can legally run in a road corresponding to a preset identification area, and the steps of obtaining image data of the preset identification area and the subsequent steps are returned to be executed so as to detect the road corresponding to the preset identification area in real time.
For example, if the vehicle information of a certain two-wheeled vehicle includes a preset legal identifier that the two-wheeled vehicle is a police motorcycle, the two-wheeled vehicle is judged to be a legal vehicle, that is, the two-wheeled vehicle can legally run in a road corresponding to a preset identification area.
The legal two-wheel vehicle and the illegal two-wheel vehicle are effectively distinguished by setting the preset legal identification, and the phenomenon that the legal two-wheel vehicle sends alarm information or carries out illegal processing is avoided.
In the embodiment, the image data of the preset identification area is acquired, the image data is identified, whether the vehicle in the image data is a two-wheel vehicle is determined, the vehicle information of the vehicle is acquired when the image data comprises the two-wheel vehicle, and when the vehicle information does not comprise the preset legal identification, the alarm information is generated according to the vehicle information, so that the accuracy and reliability of identifying whether the two-wheel vehicle exists in the motor vehicle lane are improved, and the efficiency of acquiring the vehicle information of the illegal two-wheel vehicle is improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.
Fig. 4 shows a structural block diagram of the vehicle identification device for vehicle lane violation provided in the embodiment of the present application, corresponding to the vehicle identification method for vehicle lane violation described in the above embodiment, and only the relevant parts to the embodiment of the present application are shown for convenience of description.
Referring to fig. 4, the motor vehicle lane violation vehicle recognition device 100 includes:
the acquiring module 101 is configured to acquire image data of a preset identification area;
the identification module 102 is configured to identify the image data, and determine whether the image data includes an identification result of the two-wheeled vehicle according to state data of a target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle;
the analysis module 103 is used for acquiring vehicle information of the two-wheel vehicle if the identification result is that the image data comprises the two-wheel vehicle;
the detection module 104 is configured to detect whether the vehicle information includes a preset legal identifier;
the generating module 105 is configured to generate warning information according to the vehicle information if the vehicle information does not include a preset legal identifier.
In one embodiment, the apparatus further comprises:
and the setting module is used for setting a corresponding preset identification area according to the actual data of the motor vehicle lane.
In one embodiment, the identification module 102 includes:
the identification unit is used for identifying the image data and obtaining state data of a target object in the image data;
the first judging unit is used for judging whether the state data meet preset conditions or not;
and the second judging unit is used for judging that the identification result is that the image data comprises the two-wheel vehicle if the state data meets the preset condition.
In one embodiment, the first determining unit includes:
the first judgment subunit is used for judging whether the definition of the target object is greater than a preset definition threshold value; and the combination of (a) and (b),
a second judging subunit, configured to judge whether the clothing data of the target object includes a safety helmet; and the combination of (a) and (b),
the third judging subunit is used for judging whether the integrity of the image characteristic value of the target object is greater than an integrity threshold of a preset image characteristic value; and (c) and (d),
the fourth judgment subunit is used for judging whether the actual distances among the target objects in the vehicle are smaller than a preset distance threshold value or not; and the combination of (a) and (b),
and the fifth judgment subunit is used for judging whether the relationship between the target object and the vehicle conforms to at least one of preset relationships.
In one embodiment, the first determining unit further includes:
the first acquisition subunit is used for acquiring the length of any reference object in the image data and the actual length of the reference object;
the first calculating subunit is used for calculating the proportion of the length of the reference object in the image data to the actual length;
the second acquisition subunit is used for acquiring virtual distances among a plurality of target objects in each vehicle in the image data;
and the second calculating subunit is used for calculating the virtual distance between the plurality of target objects in each vehicle according to the proportion and the image data to obtain the actual distance between the plurality of target objects in each vehicle.
In one embodiment, the apparatus further comprises:
and the judging module is used for judging that the two-wheel vehicle is a legal vehicle if the vehicle information comprises a preset legal identifier, and returning to execute the acquisition of the image data of the preset identification area.
In the embodiment, the image data of the preset identification area is acquired, the image data is identified, whether the vehicle in the image data is a two-wheel vehicle is determined, the vehicle information of the vehicle is acquired when the image data comprises the two-wheel vehicle, and when the vehicle information does not comprise the preset legal identification, the alarm information is generated according to the vehicle information, so that the accuracy and reliability of identifying whether the two-wheel vehicle exists in the motor vehicle lane are improved, and the efficiency of acquiring the vehicle information of the illegal two-wheel vehicle is improved.
It should be noted that, for the information interaction, execution process, and other contents between the above-mentioned devices/units, the specific functions and technical effects thereof are based on the same concept as those of the embodiment of the method of the present application, and specific reference may be made to the part of the embodiment of the method, which is not described herein again.
Fig. 5 is a schematic structural diagram of a terminal device according to an embodiment of the present application. As shown in fig. 5, the terminal device 5 of this embodiment includes: at least one processor 50 (only one shown in fig. 5), a memory 51, and a computer program 52 stored in the memory 51 and operable on the at least one processor 50, wherein the processor 50 implements the steps of any of the above-mentioned various embodiments of the method for identifying a vehicle with a motor lane violation when executing the computer program 52.
The terminal device 5 may be a desktop computer, a notebook, a palm computer, a cloud server, or other computing devices. The terminal device may include, but is not limited to, a processor 50, a memory 51. Those skilled in the art will appreciate that fig. 5 is only an example of the terminal device 5, and does not constitute a limitation to the terminal device 5, and may include more or less components than those shown, or combine some components, or different components, such as an input-output device, a network access device, and the like.
The Processor 50 may be a Central Processing Unit (CPU), and the Processor 50 may be other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 51 may in some embodiments be an internal storage unit of the terminal device 5, such as a hard disk or a memory of the terminal device 5. The memory 51 may also be an external storage device of the terminal device 5 in other embodiments, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like, which are provided on the terminal device 5. Further, the memory 51 may also include both an internal storage unit and an external storage device of the terminal device 5. The memory 51 is used for storing an operating system, an application program, a BootLoader (BootLoader), data, and other programs, such as program codes of the computer program. The memory 51 may also be used to temporarily store data that has been output or is to be output.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only used for distinguishing one functional unit from another, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
An embodiment of the present application further provides a terminal device, where the terminal device includes: at least one processor, a memory, and a computer program stored in the memory and executable on the at least one processor, the processor implementing the steps of any of the various method embodiments described above when executing the computer program.
The embodiments of the present application further provide a computer-readable storage medium, where a computer program is stored, and when the computer program is executed by a processor, the computer program implements the steps in the above-mentioned method embodiments.
The embodiments of the present application provide a computer program product, which when running on a mobile terminal, enables the mobile terminal to implement the steps in the above method embodiments when executed.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, all or part of the processes in the methods of the embodiments described above can be implemented by hardware related to instructions of a computer program, which can be stored in a computer readable storage medium, and when the computer program is executed by a processor, the steps of the methods described above can be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include at least: any entity or device capable of carrying computer program code to a photographing apparatus/terminal apparatus, a recording medium, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Such as a usb-disk, a removable hard disk, a magnetic or optical disk, etc. In certain jurisdictions, computer-readable media may not be an electrical carrier signal or a telecommunications signal in accordance with legislative and patent practice.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/network device and method may be implemented in other ways. For example, the above-described apparatus/network device embodiments are merely illustrative, and for example, the division of the modules or units is only one logical function division, and other divisions may be realized in practice, for example, multiple units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (8)
1. A method for identifying illegal vehicles on a motor vehicle lane is characterized by comprising the following steps:
acquiring image data of a preset identification area; the image data comprises pictures or videos of vehicles;
identifying the image data, and determining whether the image data comprises an identification result of the two-wheel vehicle according to the state data of the target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle; the target object comprises a driver or a passenger; the definition threshold is the average value of the definitions of other areas except the target object face range in the vehicle in the image data;
if the identification result is that the image data comprises a two-wheel vehicle, vehicle information of the two-wheel vehicle is obtained;
detecting whether the vehicle information comprises a preset legal identification;
if the vehicle information does not include a preset legal identifier, generating alarm information according to the vehicle information;
the identifying the image data and determining whether the image data includes the identification result of the two-wheel vehicle according to the state data of the target object in the image data includes:
identifying the image data to obtain state data of a target object in the image data;
judging whether the state data meet preset conditions or not;
if the state data meet the preset conditions, judging that the identification result is that the image data comprises the two-wheel vehicle;
wherein, the judging whether the state data accords with a preset condition comprises:
judging whether the definition of the target object is greater than a preset definition threshold value or not; and the combination of (a) and (b),
judging whether the clothing data of the target object comprises a safety helmet or not; and the combination of (a) and (b),
judging whether the integrity of the image characteristic value of the target object is greater than an integrity threshold of a preset image characteristic value; and the combination of (a) and (b),
judging whether the actual distances among a plurality of target objects in the vehicle are smaller than a preset distance threshold value or not; and the combination of (a) and (b),
and judging whether the relation between the target object and the vehicle conforms to at least one of preset relations.
2. The method for recognizing a vehicle violating a motor lane as claimed in claim 1, wherein said obtaining of image data of the predetermined recognition area comprises:
and setting a corresponding preset identification area according to the actual data of the motor vehicle lane.
3. The method of identifying a motor vehicle lane violation according to claim 1, wherein said determining whether an actual distance between a plurality of target objects within the vehicle is less than a preset distance threshold comprises:
acquiring the length of any reference object in the image data and the actual length of the reference object;
calculating the ratio of the length of the reference object in the image data to the actual length;
acquiring virtual distances between a plurality of target objects in each vehicle in the image data;
and calculating according to the proportion and the virtual distance between the target objects in each vehicle in the image data to obtain the actual distance between the target objects in each vehicle.
4. The method for recognizing a vehicle violating a motor lane as claimed in claim 1, wherein said detecting whether said vehicle information includes a preset legal sign comprises:
and if the vehicle information comprises a preset legal identification, judging that the two-wheel vehicle is a legal vehicle, and returning to execute the acquisition of the image data of the preset identification area.
5. An apparatus for recognizing an illegal vehicle on a motor vehicle lane, comprising:
the acquisition module is used for acquiring image data of a preset identification area; the image data comprises pictures or videos of vehicles;
the identification module is used for identifying the image data and determining whether the image data comprises an identification result of the two-wheel vehicle according to the state data of the target object in the image data; wherein the state data comprises definition, clothing data, integrity of image characteristic values, actual distances among a plurality of target objects in the vehicle and the position relation between the target objects and the vehicle; the target object comprises a driver or a passenger; the definition threshold is the average value of the definitions of other areas except the target object face range in the vehicle in the image data;
the analysis module is used for acquiring vehicle information of the two-wheel vehicle if the identification result is that the image data comprises the two-wheel vehicle;
the detection module is used for detecting whether the vehicle information comprises a preset legal identification;
the generating module is used for generating warning information according to the vehicle information if the vehicle information does not comprise a preset legal identifier;
wherein the identification module comprises:
the identification unit is used for identifying the image data and obtaining state data of a target object in the image data;
the first judging unit is used for judging whether the state data meet preset conditions or not;
the second judging unit is used for judging that the identification result is that the image data comprises the two-wheel vehicle if the state data meets the preset condition;
wherein the first judging unit includes:
the first judgment subunit is used for judging whether the definition of the target object is greater than a preset definition threshold value; and the combination of (a) and (b),
a second judging subunit, configured to judge whether the clothing data of the target object includes a safety helmet; and the combination of (a) and (b),
the third judging subunit is used for judging whether the integrity of the image characteristic value of the target object is greater than an integrity threshold of a preset image characteristic value; and the combination of (a) and (b),
the fourth judgment subunit is used for judging whether the actual distances among the target objects in the vehicle are smaller than a preset distance threshold value or not; and the combination of (a) and (b),
and the fifth judging subunit is used for judging whether the relationship between the target object and the vehicle conforms to at least one of preset relationships.
6. The apparatus for recognizing a vehicle as set forth in claim 5, further comprising:
and the setting module is used for setting a corresponding preset identification area according to the actual data of the motor vehicle lane.
7. A terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the method according to any of claims 1 to 4 when executing the computer program.
8. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1 to 4.
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