CN111860384B - Vehicle type recognition method - Google Patents
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- CN111860384B CN111860384B CN202010732269.4A CN202010732269A CN111860384B CN 111860384 B CN111860384 B CN 111860384B CN 202010732269 A CN202010732269 A CN 202010732269A CN 111860384 B CN111860384 B CN 111860384B
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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
- G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
- G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
- G06V20/584—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads of vehicle lights or traffic lights
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30248—Vehicle exterior or interior
- G06T2207/30252—Vehicle exterior; Vicinity of vehicle
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
- G06V20/625—License plates
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V2201/00—Indexing scheme relating to image or video recognition or understanding
- G06V2201/08—Detecting or categorising vehicles
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Abstract
The invention discloses a vehicle type identification method, which comprises the following steps: s01: the vehicle enters the shooting range of the camera, the camera shoots M frames of vehicle images, and the M frames of vehicle images are transmitted to the synthesis unit; s02: the synthesis unit splices the shot M frames of vehicle images into a complete vehicle side view and transmits the vehicle side view to the feature extraction unit; s03: the characteristic extraction unit outputs the vehicle type, the axle information and the vehicle length information and transmits the information to the vehicle type establishing unit; s04: the vehicle type establishing unit outputs vehicle type information according to the vehicle type, the axle information and the vehicle length information. The invention can accurately capture the characteristic points of the vehicle, quickly restore the side images of the vehicle and accurately identify the vehicle type.
Description
Technical Field
The invention relates to the field of vehicle management, in particular to a vehicle type identification method.
Background
With the intelligent development of vehicle management, vehicle type identification for vehicles becomes more and more important. The vehicle management system needs to judge the specific information of the vehicle according to the vehicle type information and then can carry out the next operation; for example, the new regulations of the department of transportation stipulate that high-speed toll collection needs to be determined based on vehicle types based on the vehicle length, the axle, and the number of passengers. The vehicle type identification method includes the steps that a vehicle image needs to be obtained on the premise of vehicle type identification, and then vehicle type information identification is carried out according to the vehicle image.
In the prior art, for obtaining an image of a vehicle, the following two methods are generally adopted: (1) and a close-range camera is arranged on the side surface of the lane, is close to the lane and is used for capturing a vehicle dynamic image and identifying vehicle information according to the characteristic points extracted from the dynamic image. Although the device and the method can capture the moving state of the vehicle from far to near or from near to far, the camera is positioned on the side of the lane, and compared with the image shot in the front, the device and the method cannot acquire accurate side features of the vehicle, so that the recognition result is influenced. (2) The method comprises the steps of arranging a plurality of cameras at a plurality of positions of a lane, splicing images shot by the plurality of cameras, and recognizing vehicle types by using the spliced complete images. Because different moving object depth distances are dynamic variables, angle errors caused by non-parallel driving of vehicles along the roadside and more or less imaging differences among a plurality of cameras, the method has the defects of low splicing precision and complex deployment and installation.
Therefore, it is urgently needed to design a method capable of accurately and quickly identifying vehicle type information.
Disclosure of Invention
The invention aims to provide a vehicle type identification method which can accurately capture vehicle characteristic points, quickly restore a vehicle side image and accurately identify a vehicle type.
In order to achieve the purpose, the invention adopts the following technical scheme: a vehicle type recognition method comprises the following steps:
s01: the vehicle enters the shooting range of the camera, the camera shoots M frames of vehicle images, and the M frames of vehicle images are transmitted to the synthesis unit; the included angle between the visual field central line of the camera and the lane is any value between 80 degrees and 100 degrees, and M is an integer greater than 0;
s02: the synthesis unit splices the shot M frames of vehicle images into a complete vehicle side view and transmits the vehicle side view to the feature extraction unit;
s03: the characteristic extraction unit outputs the vehicle type, the axle information and the vehicle length information and transmits the information to the vehicle type establishing unit;
s04: the vehicle type establishing unit outputs vehicle type information according to the vehicle type, the axle information and the vehicle length information.
Further, the feature extraction unit in step S03 includes a vehicle length identification subunit, an axle identification subunit, and a classification subunit, where the vehicle length identification subunit outputs the vehicle length information, the axle identification subunit outputs the axle information, and the classification subunit outputs the vehicle type.
Further, the step of outputting the vehicle length information by the vehicle length identifying subunit specifically includes the following steps:
s031: the vehicle length identification subunit acquires the vehicle length under a two-dimensional image coordinate system according to the vehicle side map;
s032: the vehicle length identification subunit converts the vehicle length under the two-dimensional image coordinate system into the vehicle length under the imaging coordinate system according to the imaging principle of the camera;
s033: and the vehicle length identification subunit converts the vehicle length in the imaging coordinate system into the vehicle length in the camera coordinate system according to the distance between the vehicle and the camera.
Further, the vehicle types include passenger cars, trucks, and special cars.
Further, the vehicle type establishing unit outputs preliminary vehicle type information according to the vehicle type, the axle information, and the vehicle length information in the step S04; and checking the preliminary vehicle type information and the license plate information mutually to obtain accurate vehicle type information.
Further, the step S04 of acquiring the preliminary vehicle type information includes the following steps:
s041: the vehicle type establishing unit judges the type of the vehicle according to the type of the vehicle;
s042: the vehicle type establishing unit judges the type of a vehicle of a corresponding type according to the axle information;
s043: the vehicle type establishing unit judges the preliminary vehicle type information of the corresponding type vehicle according to the vehicle length information.
Further, the method for acquiring accurate vehicle type information in step S04 includes: if the color and the characters of the license plate in the license plate information correspond to the preliminary vehicle type information, the accurate license plate information is consistent with the preliminary license plate information; and if the colors and characters of the license plate in the license plate information do not correspond to the initial vehicle type information, the vehicle type establishing unit determines the accurate vehicle type information again according to the sequence of the vehicle type, the axle information, the license plate information and the vehicle length information.
Further, the step S02 specifically includes:
s021: the M frames of vehicle images are stored in a queue according to the time sequence;
s022: the identification subunit identifies the vehicle head and the vehicle body in the M frames of vehicle images;
s023: the speed calculation subunit calculates a vehicle speed value according to the M frames of vehicle images;
s024: the identifying subunit identifies the vehicle tail;
s025: and the splicing subunit splices a complete vehicle side view according to the vehicle speed value and the positions of the vehicle head, the vehicle body and the vehicle tail.
Further, the respective feature points extracted in the speed calculation subunit are located in the same side of the vehicle, and the side is at equal distances from the camera everywhere.
Further, when the vehicle tail is recognized by the recognition subunit in step S024, the recognition subunit adds a disconnection mark to the position of the vehicle tail, and the synthesis unit splices the vehicle images before the disconnection mark in step S025.
The invention has the following beneficial effects: the included angle between the visual field center line of the camera and the lane is set to be any value between 80 degrees and 100 degrees, and the multiple frames of shot vehicle images are spliced into a complete vehicle side view; on the basis of the vehicle side view, the vehicle type establishing unit acquires the vehicle type information according to the vehicle type, the axle information and the vehicle length information.
Drawings
FIG. 1 is a flow chart of a vehicle identification method of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings.
As shown in fig. 1, the vehicle type recognition method provided by the invention comprises the following steps:
s01: when the vehicle enters the shooting range of the camera, the camera shoots M frames of vehicle images and transmits the M frames of vehicle images to the synthesis unit; the included angle between the visual field central line of the camera and the lane is any value between 80 degrees and 100 degrees, and M is an integer larger than 0. Preferably, the included angle between the visual field center line of the camera and the lane is 90 degrees, and the visual field boundary of the camera cannot cover the side panorama of the vehicle, so that the camera needs to continuously shoot M frames of vehicle images, and the synthesis unit splices the vehicle images together to form a complete vehicle side image.
S02: the synthesis unit splices the shot M frames of vehicle images into a complete vehicle side view and transmits the vehicle side view to the feature extraction unit.
S02: the synthesis unit splices the shot M frames of vehicle images into a complete vehicle side view and transmits the vehicle side view to the identification unit; the method specifically comprises the following steps:
s021: the M frames of vehicle images are stored in a queue according to the time sequence;
s022: the identification subunit identifies the vehicle head and the vehicle body in the M frames of vehicle images;
s023: the speed calculation subunit calculates a vehicle speed value according to the M frames of vehicle images; specifically, the speed calculation subunit extracts feature points in the M frames of vehicle images, for example, the same feature point appears at different positions in different vehicle images, and according to the change of the position of the feature point and the time difference between the two vehicle images, the speed calculation subunit can calculate the vehicle speed value. It is worth noting that: each feature point extracted from the speed calculation subunit is positioned in the same side of the vehicle, and the distances from the side to the camera are equal everywhere; only by ensuring that the positions of the characteristic points in each image used for splicing the images in the actual vehicle are the same as the depth of field of the camera, the spliced images can be ensured to be complete and not deformed. The reason is that in the process of changing the two-dimensional plane graph from the three-dimensional structure of the vehicle to the image, only the characteristic points on the same side face at the same depth position with the camera can be used as a spliced bridge, and the spliced image can accurately reflect the shape of the vehicle.
S024: the identifying subunit identifies the vehicle tail; when the vehicle tail is identified, the identifying subunit adds a disconnection mark at the position of the vehicle tail, namely, the vehicle image is disconnected with the subsequent vehicle image.
S025: splicing a complete vehicle side map by the splicing subunit according to the vehicle speed value and the positions of the vehicle head, the vehicle body and the vehicle tail, wherein when splicing, an image of the vehicle head position is taken as a splicing start, an image of the vehicle tail position is taken as a splicing end, then the repeated parts among the same characteristic points are covered by combining the vehicle running speed and the shooting frame rate of the camera, and the same characteristic points are taken as bridges, and splicing a vehicle side panoramic map from the vehicle head to the vehicle tail by the splicing subunit; and splicing the panoramic pictures of the vehicle side from the vehicle head to the vehicle tail by the splicing subunits.
S03: the feature extraction unit outputs the vehicle type, the axle information and the vehicle length information, and transmits the information to the vehicle type establishing unit. The feature extraction unit comprises a conductor identification subunit, an axle identification subunit and a classification subunit, wherein the conductor identification subunit outputs conductor information, the axle identification subunit outputs axle information, the classification subunit outputs vehicle types, and the vehicle types can specifically include passenger cars, trucks, special cars and the like.
Specifically, the classification subunit may extract the characteristics of the entire vehicle from the vehicle side view, and the specific characteristics of the entire vehicle may be, but are not limited to, inherent characteristics of a passenger vehicle, a truck, a special vehicle, or the like, or inherent characteristics of a head, a tail, or the like; and judging the type of the vehicle according to the extracted characteristics of the whole vehicle, namely determining whether the vehicle belongs to a passenger car, a truck or a special vehicle.
In the actual judging process, the characteristics of the passenger car, the freight car and the special car can be stored in the storage subunit, the classification subunit compares the extracted characteristics of the whole car with the characteristics stored in the storage subunit, and then determines which characteristic in the storage subunit is the closest to the extracted characteristics of the whole car, and further determines the type of the car. The classification subunit determines which kind of special vehicle the vehicle specifically belongs to according to the characteristics of the different kinds of special vehicles.
Specifically, the process of outputting the vehicle length information by the vehicle length identifying subunit is as follows:
s031: the vehicle length identification subunit acquires the vehicle length under a two-dimensional image coordinate system according to the positions of the head and the tail of the vehicle in the vehicle side view; the vehicle length in the two-dimensional image coordinate system is based on the vehicle side view, and specifically, a certain top and bottom of the vehicle side view can be used as a coordinate origin.
S032: the vehicle length identification subunit converts the vehicle length under the two-dimensional image coordinate system into the vehicle length under the imaging coordinate system according to the imaging principle of the camera; the vehicle length in the imaging coordinate system is based on the imaging plane, and specifically, the origin of the imaging plane can be the origin of coordinates.
S033: and the vehicle length identification subunit converts the vehicle length in the two-dimensional image coordinate system into the vehicle length in the camera coordinate system according to the distance between the vehicle and the camera. The distance between the vehicle and the camera can be acquired by adopting an ultrasonic range finder of the camera accessory, the ultrasonic wave can acquire the depth of the position of the vehicle from the camera, and then the vehicle length under the two-dimensional image coordinate system is converted into the vehicle length under the camera coordinate system according to the depth, the imaging parameter and the camera parameter, the vehicle length under the camera coordinate system takes the camera position as a reference, and specifically, the camera position can be taken as a coordinate origin.
The axle identifying subunit can extract the axle information from the vehicle side view, identify the axle information and further obtain the corresponding axle information according to the identification result. The axle in the present invention refers to the number of axles located at different positions in the front and rear of the vehicle in the side view of the vehicle.
S04: the vehicle type establishing unit outputs preliminary vehicle type information according to the vehicle type, the axle information and the vehicle length information; and checking the preliminary vehicle type information and the license plate information mutually to obtain accurate vehicle type information. The method specifically comprises the following steps:
s041: the vehicle type establishing unit judges the type of the vehicle according to the type of the vehicle; the vehicle type in a particular embodiment may be one of a passenger car, a truck, or a special car.
S042: the vehicle type establishing unit judges the type of the corresponding type of vehicle according to the axle information; the vehicle signals include passenger one, passenger two, passenger three, passenger four or cargo one, cargo two, cargo three, cargo four, cargo five, cargo six, and so on.
Specifically, when the vehicle type establishing unit determines that the vehicle is a truck, the step determines the type of the vehicle according to the axle information output by the axle identifying subunit, for example, if the axle is less than or equal to 2, the vehicle type is uniformly determined as cargo one or cargo two; if the axle is equal to 3, uniformly judging that the vehicle type is cargo three; if the axle is equal to 4, uniformly judging that the vehicle type is cargo four; if the axle is equal to 5, uniformly judging that the vehicle type is cargo five; if the axle is larger than 6, the vehicle type is judged to be six goods in a unified way.
S043: the vehicle type establishing unit judges the initial vehicle type information of the vehicle with the corresponding model according to the vehicle length information, and the vehicle type information is further determined by combining the vehicle length information on the basis of the vehicle model.
Specifically, when the vehicle type establishing unit determines that the vehicle is a passenger car and the number of axles is 2, the step determines the vehicle type information according to the vehicle length information output by the vehicle length identifying subunit. For example, a passenger car having a car length of more than 0 and x or less may be determined as a first passenger, a vehicle having a car length of more than x and y meters may be determined as a second passenger, a vehicle having a car length of more than y meters and less than z may be determined as a third passenger, and a vehicle having a car length of more than z may be determined as a fourth passenger. Wherein x is more than 0 and y is more than z.
The determination of the vehicle type information in the invention follows the following rules: the judgment of the vehicle type is prioritized over the axle judgment and the vehicle length judgment, and the axle judgment is prioritized over the vehicle length judgment.
When the vehicle type is a truck, the license plate judgment process can be added to the determination of the vehicle type information, and the following rules are specifically followed: the judgment of the vehicle type is preferably combined with the judgment of an axle and the judgment of a vehicle length, the judgment of the axle is preferably carried out on the judgment of a license plate, and the judgment of the license plate is prior to the judgment of the vehicle length. This is because the license plate information is obviously different for the first cargo and the second cargo when the number of axles is the same: the first goods is blue cards and the second goods is yellow cards.
In the actual vehicle type recognition process, in order to obtain accurate vehicle type information, the preliminary vehicle type information and the license plate information of the vehicle can be checked, wherein the license plate information obtaining method can adopt any method in the prior art, and the verification process is as follows: if the color and the characters of the license plate in the license plate information correspond to the preliminary vehicle type information, for example, the color of the license plate is blue, and the vehicle type information is goods one, the accurate license plate information is consistent with the preliminary license plate information. If the license plate obtained from the license plate information identification result comprises colors and characters, the type of the vehicle is obviously inconsistent with the initial license plate information according to the license plate big data statistical rule, for example, the license plate information is a yellow plate, and the vehicle type information is a passenger vehicle or a cargo vehicle I; the vehicle type establishing unit determines the accurate vehicle type information again according to the vehicle type, the axle information and the license plate information; specifically, the vehicle type establishing unit judges the type of the vehicle firstly, then judges the type of the vehicle according to the axle information, further determines the type of the vehicle according to the license plate information, and finally determines the accurate vehicle type information according to the vehicle length information. That is, when the license plate information is inconsistent with the preliminary vehicle type information, the license plate judgment is prior to the vehicle length judgment.
The included angle between the visual field center line of the camera and the lane is set to be any value between 80 degrees and 100 degrees, and the multiple frames of shot vehicle images are spliced into a complete vehicle side view; on the basis of the vehicle side view, the vehicle type establishing unit acquires the vehicle type information according to the vehicle type, the axle information and the vehicle length information.
The above description is only a preferred embodiment of the present invention, and the embodiment is not intended to limit the scope of the present invention, so that all equivalent structural changes made by using the contents of the specification and the drawings of the present invention should be included in the scope of the appended claims.
Claims (7)
1. A vehicle type recognition method is characterized by comprising the following steps:
s01: the vehicle enters the shooting range of the camera, the camera shoots M frames of vehicle images, and the M frames of vehicle images are transmitted to the synthesis unit; the included angle between the visual field central line of the camera and the lane is any value between 80 degrees and 100 degrees, and M is an integer greater than 0;
s02: the synthesis unit splices the shot M frames of vehicle images into a complete vehicle side view and transmits the vehicle side view to the feature extraction unit;
s03: the characteristic extraction unit outputs the vehicle type, the axle information and the vehicle length information and transmits the information to the vehicle type establishing unit; the vehicle type is one of a passenger car, a truck or a special vehicle;
s04: the vehicle type establishing unit outputs preliminary vehicle type information according to the vehicle type, the axle information and the vehicle length information, the preliminary vehicle type information and the license plate information are checked with each other, and accurate vehicle type information is obtained;
the method for acquiring the preliminary vehicle type information comprises the following steps:
the vehicle type establishing unit judges the type of the vehicle according to the type of the vehicle; when the vehicle type building unit judges that the vehicle is a truck, judging that the type of the vehicle is cargo one, cargo two, cargo three, cargo four, cargo five or cargo six according to the axle information; when the vehicle type building unit judges that the vehicle is a passenger car, judging that the type of the vehicle is a passenger one or a passenger two or a passenger three or a passenger four according to the axle information and the length information;
the method for acquiring accurate vehicle type information comprises the following steps: if the color and the characters of the license plate in the license plate information correspond to the preliminary vehicle type information, the accurate license plate information is consistent with the preliminary license plate information; and if the colors and characters of the license plate in the license plate information do not correspond to the initial vehicle type information, the vehicle type establishing unit determines the accurate vehicle type information again according to the sequence of the vehicle type, the axle information, the license plate information and the vehicle length information.
2. The vehicle type recognition method according to claim 1, wherein the feature extraction unit in step S03 includes a vehicle length recognition subunit, an axle recognition subunit and a classification subunit, the vehicle length recognition subunit outputs the vehicle length information, the axle recognition subunit outputs the axle information, and the classification subunit outputs the vehicle type.
3. The vehicle type identification method according to claim 2, wherein the driver identifying subunit outputs the driver information specifically includes the steps of:
s031: the vehicle length identification subunit acquires the vehicle length under a two-dimensional image coordinate system according to the vehicle side map;
s032: the vehicle length identification subunit converts the vehicle length under the two-dimensional image coordinate system into the vehicle length under the imaging coordinate system according to the imaging principle of the camera;
s033: and the vehicle length identification subunit converts the vehicle length in the imaging coordinate system into the vehicle length in the camera coordinate system according to the distance between the vehicle and the camera.
4. The vehicle type identification method according to claim 1, wherein the vehicle types include passenger cars, trucks, and special cars.
5. The vehicle type identification method according to claim 1, wherein the step S02 specifically includes:
s021: the M frames of vehicle images are stored in a queue according to the time sequence;
s022: the identification subunit identifies the vehicle head and the vehicle body in the M frames of vehicle images;
s023: the speed calculation subunit calculates a vehicle speed value according to the M frames of vehicle images;
s024: the identifying subunit identifies the vehicle tail;
s025: and splicing the complete vehicle side face map by the splicing subunit according to the vehicle speed value and the positions of the vehicle head, the vehicle body and the vehicle tail.
6. The vehicle type recognition method according to claim 5, wherein the respective feature points extracted in the speed calculation subunit are located in the same side of the vehicle, and the side is at equal distances from the camera everywhere.
7. The vehicle type recognition method according to claim 5, wherein when the recognition subunit recognizes the vehicle tail in step S024, the recognition subunit adds a disconnection mark to a position of the vehicle tail, and the synthesis unit splices the vehicle images before the disconnection mark in step S025.
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| CN112966582A (en) * | 2021-02-26 | 2021-06-15 | 北京卓视智通科技有限责任公司 | Vehicle type three-dimensional recognition method, device and system, electronic equipment and storage medium |
| CN113822866A (en) * | 2021-09-23 | 2021-12-21 | 深圳爱莫科技有限公司 | Widely-adaptive axle number identification method, system, equipment and storage medium |
| CN114332681A (en) * | 2021-12-08 | 2022-04-12 | 上海高德威智能交通系统有限公司 | Vehicle identification method and device |
| CN114550141A (en) * | 2022-03-07 | 2022-05-27 | 上海闪马智能科技有限公司 | Vehicle identification system and method |
| CN114689156A (en) * | 2022-04-02 | 2022-07-01 | 重庆道盛科技有限公司 | High-precision dynamic and static weighing method, device, equipment and system |
| CN114757833B (en) * | 2022-06-15 | 2022-09-27 | 北京中瑞方兴科技有限公司 | Vehicle side image splicing method and system |
| CN116189113B (en) * | 2022-12-29 | 2024-03-08 | 北京中科神通科技有限公司 | Truck type recognition method and system |
| CN116977894A (en) * | 2023-06-21 | 2023-10-31 | 北京卓视智通科技有限责任公司 | Vehicle information identification method, system, storage medium and electronic equipment |
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