WO2023273158A1

WO2023273158A1 - Method and apparatus for determining operating range of camera in cooperative vehicle infrastructure and roadside device

Info

Publication number: WO2023273158A1
Application number: PCT/CN2021/135146
Authority: WO
Inventors: 邓烽; 时一峰; 苑立彬
Original assignee: 阿波罗智联(北京)科技有限公司
Priority date: 2021-06-29
Filing date: 2021-12-02
Publication date: 2023-01-05
Also published as: CN113470103B; CN113470103A

Abstract

The present disclosure relates to the technical field of intelligent traffic, and specifically relates to the technical field of visual processing. Disclosed are a method and apparatus for determining the operating range of a camera in cooperative vehicle infrastructure and a roadside device. The specific implementation solution comprises: first obtaining an acquisition image and a pixel focal length of the camera; then cutting a cut image comprising a target object from the acquisition image; and finally determining the maximum operating range of the camera on the basis of the pixel focal length and the number of pixels per unit distance of the cut image, the number of pixels per unit distance of the cut image being the number of pixels comprised in the minimum unit that can be recognized by a detection model in the cut image. The operating range of the camera can be automatically adjusted by cutting the acquisition image, and the flexibility of adjusting the operating range of the camera is improved.

Description

Method, device and roadside equipment for determining camera action distance in vehicle-road coordination

This patent application claims the priority of the Chinese patent application filed on June 29, 2021 with the application number 202110724108.5 and the title of the invention is "Method, Device and Roadside Equipment for Determining Camera Action Distance in Vehicle-Infrastructure Coordination". This application is incorporated by reference in its entirety.

technical field

The present disclosure relates to the technical field of intelligent transportation, in particular to the technical field of visual processing, and in particular to a method, a device and a roadside device for determining a camera action distance in vehicle-road coordination.

Background technique

In the construction of vehicle-road coordination V2X infrastructure, the roadside perception system provides beyond-the-horizon perception information for vehicle-road coordination. The camera is one of the most important sensors of the roadside perception system, and its working distance is an important index to measure the perception system.

The related method is to directly use the original image provided by the camera, perform de-distortion according to the internal parameters of the camera, and then perform two-dimensional plane detection and three-dimensional perception positioning in the entire image, and the working distance is determined directly based on the original image.

Contents of the invention

The present disclosure provides a method, device, electronic equipment, storage medium, computer program product, roadside equipment, and cloud control platform for determining camera action distance in vehicle-road coordination.

According to an aspect of the present disclosure, there is provided a method for determining the working distance of a camera in vehicle-road coordination, the method comprising: acquiring a captured image and a pixel focal length of the camera; intercepting an intercepted image including a target object from the captured image; based on the pixel focal length and The number of pixels per unit distance of the intercepted image determines the maximum operating distance of the camera, wherein the number of pixels per unit distance of the intercepted image is the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image.

According to another aspect of the present disclosure, there is provided a device for determining the working distance of a camera in vehicle-road coordination, the device includes: an acquisition module configured to acquire the captured image and pixel focal length of the camera; an intercepting module configured to obtain the captured image from The middle interception includes the intercepted image of the target object; the determination module is configured to determine the maximum operating distance of the camera based on the pixel focal length and the number of pixels per unit distance of the intercepted image, wherein the number of pixels per unit distance of the intercepted image is the number of pixels that can be detected in the intercepted image The number of pixels included in the smallest unit recognized by the model.

According to another aspect of the present disclosure, an electronic device is provided, the electronic device includes at least one processor; and a memory connected in communication with the at least one processor; wherein, the memory stores instructions executable by the at least one processor , the instructions are executed by at least one processor, so that the at least one processor can execute the above-mentioned method for determining the working distance of a camera in vehicle-road coordination.

According to another aspect of the present disclosure, an embodiment of the present application provides a computer-readable medium, on which computer instructions are stored, and the computer instructions are used to enable a computer to execute the above-mentioned method for determining the working distance of a camera in vehicle-road coordination.

According to another aspect of the present disclosure, an embodiment of the present application provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, implements the above-mentioned method for determining the working distance of a camera in vehicle-road coordination.

According to another aspect of the present disclosure, an embodiment of the present application provides a roadside device, including the above-mentioned electronic device.

According to another aspect of the present disclosure, an embodiment of the present application provides a cloud control platform, including the above-mentioned electronic device.

It should be understood that what is described in this section is not intended to identify key or important features of the embodiments of the present disclosure, nor is it intended to limit the scope of the present disclosure. Other features of the present disclosure will be readily understood through the following description.

Description of drawings

The accompanying drawings are used to better understand the present solution, and do not constitute a limitation to the present disclosure. in:

FIG. 1 is a flowchart of an embodiment of a method for determining a camera range in vehicle-road coordination according to the present disclosure;

FIG. 2 is a schematic diagram of an application scenario of a method for determining a camera action distance in vehicle-road coordination according to the present disclosure;

Fig. 3 is a flow chart of an embodiment of obtaining the number of pixels per unit distance corresponding to the intercepted image according to the present disclosure;

FIG. 4 is a flowchart of one embodiment of determining a geographic location of a target object according to the present disclosure;

Fig. 5 is a schematic structural diagram of an embodiment of a device for determining a camera action distance in vehicle-road coordination according to the present disclosure;

Fig. 6 is a block diagram of an electronic device used to implement the method for determining the working distance of a camera in vehicle-road coordination according to an embodiment of the present disclosure.

detailed description

Exemplary embodiments of the present disclosure are described below in conjunction with the accompanying drawings, which include various details of the embodiments of the present disclosure to facilitate understanding, and they should be regarded as exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.

Referring to FIG. 1 , FIG. 1 shows a schematic flowchart 100 of an embodiment of a method for determining a camera range in vehicle-road coordination that can be applied in the present disclosure. The method for determining the working distance of the camera in the vehicle-road coordination includes the following steps:

Step 110, acquire the captured image and pixel focal length of the camera.

In this embodiment, the executing subject of the method for determining the working distance of the camera (such as a terminal device or a server) may receive the camera parameters input by the user, and after obtaining the camera parameters, further calculate according to the camera parameters to obtain the pixel focal length of the camera , the pixel focal length is the focal length in pixels. As an example, the above execution subject can obtain the physical focal length of the camera and the physical size of the pixels, and obtain the pixel focal length of the camera according to the calculation formula of the pixel focal length: pixel focal length=physical focal length/pixel physical size.

The above execution subject may also use the camera to acquire the captured image of the camera, and the captured image may include the target object for distance measurement.

Step 120, intercepting an intercepted image including the target object from the collected image.

In this embodiment, the execution subject may use an image processing method to perform image processing on the acquired image. The image processing method may include image correction, image filtering, image grayscale, image enhancement, and the like. The above-mentioned executive body can perform image segmentation on the processed collected image. Image segmentation is to divide the image into several specific regions with unique properties. Image segmentation methods mainly include threshold-based segmentation methods, region-based segmentation methods, and edge-based segmentation methods. In the field of deep learning, multi-layer neural network models, such as deep neural network and convolutional neural network, can be used for image segmentation. The above-mentioned executive body can use image segmentation methods to collect images Segmentation is performed, and an intercepted image including the target object is intercepted from the collected image.

The execution subject may also perform target object recognition on the acquired image to determine the position information of the target object in the image. Then the execution subject intercepts the captured image according to the determined position information, and obtains the captured image including the target object.

Step 130, based on the pixel focal length and the number of pixels per unit distance of the intercepted image, determine the maximum working distance of the camera.

In this embodiment, after the execution subject obtains the pixel focal length of the camera, it can obtain the number of pixels per unit distance corresponding to the intercepted image through calculation based on the collected image and the intercepted image or calculation based on the intercepted image and the detection model, and the corresponding pixel number of the intercepted image The number of pixels per unit distance may be the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image, and the detection model is a model used to detect the target object. The detection model corresponds to the smallest target object that can be detected. According to the actual size of the smallest target object, the number of pixels corresponding to the smallest target object can be determined within a unit distance. The number of pixels per unit distance of the image.

Alternatively, the execution subject may determine the number of pixels per unit distance corresponding to the captured image, and determine the ratio between the two according to the resolution of the collected image and the resolution of the intercepted image. The number of distance pixels determines the number of pixels per unit distance corresponding to the intercepted image.

After the execution subject obtains the pixel focal length of the camera and the number of pixels per unit distance corresponding to the intercepted image, it can calculate the maximum operating distance of the camera according to the ratio between the focal length of the pixel and the number of pixels per unit distance. The maximum operating distance can be The farthest distance for the target object to be detected. The above-mentioned execution subject can calculate the maximum operating distance of the camera according to the formula, which can be:

max_distance=focal/min_pixels_per_meter

Among them, max_distance indicates the maximum operating distance of the camera, focal indicates the pixel focal length of the camera, and min_pixels_per_meter indicates the number of pixels per unit distance corresponding to the intercepted image.

As an example, the resolution of the captured image is (w1, h1), the number of pixels per unit distance corresponding to the captured image is min1, the intercepted image is intercepted from the captured image, and the resolution of the captured image is (w2, h2), then the above The execution subject can determine the ratio between the resolution of the captured image and the resolution of the intercepted image as w1/w2, and determine that the number of pixels per unit distance corresponding to the intercepted image is min1/(w1/w2). The above-mentioned executive body can determine that the maximum working distance of the camera is max_distance1=focal/min1 under the captured image; the maximum working distance of the camera is max_distance=focal/min1/(w1/w2) under the intercepted image.

Continue to refer to FIG. 2 , which is a schematic diagram of an application scenario of the method for determining the working distance of a camera in vehicle-road coordination according to this embodiment. In the application scenario of FIG. 2, the terminal 201 can display the physical parameter input interface of the camera to the user through the display screen, and the user can input the physical parameter of the camera in the physical parameter input interface, and the terminal 201 can obtain the pixel focal length of the camera according to the physical parameter and an acquired image including an object of interest. The terminal 201 may perform image processing on the collected image, and intercept the intercepted image including the target object from the acquired collected image, and then the terminal 201 calculates the maximum working distance of the camera according to the pixel focal length of the camera and the number of pixels per unit distance of the intercepted image.

The method for determining the working distance of the camera in the vehicle-road coordination provided by the embodiments of the present disclosure obtains the captured image and the pixel focal length of the camera, and then captures the captured image including the target object from the captured image, and finally based on the pixel focal length and the unit distance of the captured image The number of pixels determines the maximum operating distance of the camera. The number of pixels per unit distance of the intercepted image is the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image. The automatic adjustment of the camera’s operating distance can be realized by intercepting the collected image. Since the intercepted image is intercepted from the collected image, there is a certain proportional relationship between the resolution of the intercepted image and the resolution of the collected image, and the number of pixels per unit distance corresponding to the intercepted image is also the same as the number of pixels per unit distance corresponding to the collected image. The proportional relationship can use image interception to realize the change of the number of pixels per unit distance, so as to realize the change of the camera's working distance, and can increase the working distance of the camera without changing the focal length of the camera, which reduces the cost of the zoom camera and improves the adjustment of the camera. Range flexibility.

As an optional implementation, the above step 110, obtaining the pixel focal length of the camera, may include the following steps: obtaining the physical focal length and imaging sensor parameters of the camera; determining the pixel of the camera based on the physical focal length, imaging sensor parameters and the resolution of the collected image focal length.

Specifically, the above-mentioned executive body can read or receive the camera parameters input by the user through the network, and obtain the physical parameters of the camera. The physical parameters of the camera can be the basic parameters for the camera to shoot, and can include imaging sensor parameters, physical focal length, shutter Some parameters such as speed are used to constant camera performance.

The above-mentioned execution subject can provide the user with an input interface of the physical parameters of the camera through a display device such as a display screen, and the user can input the physical parameters of the camera in the input interface, so that the above-mentioned execution subject can obtain the physical focal length and imaging sensor parameters of the camera through user input ; Or, the above execution subject can read the physical parameters of the camera from the network according to the camera parameters stored in the network, and obtain the physical focal length and imaging sensor parameters of the camera.

The physical parameters of the camera acquired by the execution subject include the physical focal length of the camera and the parameters of the imaging sensor, which can further determine the resolution of the captured image of the camera. The above execution subject can determine the pixel focal length of the camera by using the pixel focal length calculation formula according to the physical focal length of the camera, the imaging sensor parameters and the resolution of the captured image. The pixel focal length calculation formula can be:

Among them, focal represents the pixel focal length of the camera, lens represents the physical focal length of the camera, img_width and img_height represent the resolution of the captured image, and sensor_size represents the imaging sensor parameters of the camera.

In this implementation, the pixel focal length of the camera is determined through the physical focal length, imaging sensor parameters and the resolution of the captured image, and the pixel focal length can be determined according to the calculation relationship among the physical focal length, imaging sensor parameters and the resolution of the captured image , improving the efficiency and accuracy of determining the focal length of a pixel.

As an optional implementation, the above step 110, obtaining the pixel focal length of the camera, may also include the following steps: determining the first internal reference matrix of the camera based on the collected image and the camera calibration algorithm; obtaining the pixel focal length of the camera from the first internal reference matrix .

Specifically, the above-mentioned executive body can obtain the captured image, use the camera calibration algorithm to calibrate the captured image, and calculate the first internal reference matrix of the camera. The first internal reference matrix can include the pixel focal length of the camera and the center of the photosensitive plate of the camera. For the coordinates in the coordinate system, for example, the above execution subject can calibrate the captured image by using the Zhang Zhengyou checkerboard calibration algorithm, and obtain the first internal parameter matrix and external parameter matrix of the camera. The above execution subject may determine the pixel focal length of the camera from the calculated first internal reference matrix.

In this implementation, the pixel focal length of the camera is determined through the camera calibration algorithm, and the pixel focal length can be determined quickly and accurately according to the collected images, which improves the efficiency and accuracy of determining the pixel focal length.

With reference to Fig. 3, Fig. 3 has shown the method step that obtains the unit distance pixel number of intercepted image, and it may comprise the following steps:

Step 310, acquiring the number of pixels per unit distance of the sample image in the detection model.

In this embodiment, the execution subject may read the detection model, and obtain the number of pixels per unit distance of the sample image in the detection model. Among them, the detection model is trained based on the sample image, and is used to detect the target object in the collected image. The detection model corresponds to the smallest target object that can be detected. According to the actual size of the smallest target object, the smallest target object can be determined. The corresponding number of pixels includes the number of pixels per unit distance, so that the number of pixels per unit distance of the sample image in the detection model can be obtained, that is, the number of pixels per unit distance of the sample image is included in the smallest unit that can be recognized by the detection model in the sample image of pixels.

Step 320, acquire the resolution of the sample image, and determine the ratio between the resolution of the sample image and the resolution of the intercepted image.

In this embodiment, the detection model can detect images with a preset resolution, and the model can be obtained through training with sample images of the same resolution. The above execution subject can obtain the resolution of the sample image in the detection model, and then determine the resolution of the intercepted image. The execution subject may calculate a ratio between the resolution of the sample image and the resolution of the intercepted image according to the resolution of the sample image and the resolution of the intercepted image.

Step 330, based on the number of pixels per unit distance and the ratio value of the sample image, the number of pixels per unit distance of the intercepted image is obtained.

In this embodiment, after the execution subject determines the ratio between the resolution of the sample image and the resolution of the intercepted image, the unit distance corresponding to the intercepted image can be calculated according to the number of pixels per unit distance of the sample image and the ratio value The number of pixels, so that the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image can be obtained.

In this implementation, the number of pixels per unit distance of the intercepted image is calculated through the proportional relationship between the sample image of the detection model and the intercepted image, so as to determine the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image , the number of pixels in the intercepted image that can be recognized by the detection model as the minimum unit distance can be obtained.

Referring to FIG. 4, FIG. 4 shows the method steps for determining the geographic location of the target object, which may include the following steps:

Step 410, based on the resolution of the intercepted image and the first internal reference matrix, determine a second internal reference matrix for the intercepted image.

In this embodiment, the execution subject can use the camera calibration algorithm to determine the first internal parameter matrix and external parameter matrix of the camera. The first internal parameter matrix includes the pixel focal length of the camera and the coordinate value of the center of the photosensitive plate of the camera in the pixel coordinate system.

The above execution subject can determine the resolution of the intercepted image, and after obtaining the first internal reference matrix, determine the coordinate value of the center of the photosensitive plate of the camera in the pixel coordinate system under the resolution of the intercepted image, that is, the coordinate value can be the intercepted image half of the resolution. The execution subject may determine the second internal reference matrix of the intercepted image according to the determined pixel focal length and the coordinate values corresponding to the intercepted image.

As an example, the above execution subject performs Zhang Zhengyou checkerboard calibration algorithm on the collected images to determine the first internal parameter matrix, and the first internal parameter matrix is:

Among them, fx and fy are the pixel focal length of the camera, cx and cy are the coordinate values of the center of the photosensitive plate of the camera in the pixel coordinate system at the resolution of the captured image. After the execution subject obtains the resolution (W, H) of the intercepted image, half of the resolution can be used as the coordinate value of the center of the photosensitive plate of the camera in the pixel coordinate system at the resolution of the intercepted image, that is, in the resolution of the intercepted image The coordinate value of the center of the photosensitive plate of the camera in the pixel coordinate system is (W/2, H/2), and the focal length of the pixel remains unchanged. Then it can be determined that the second internal parameter matrix corresponding to the intercepted image is:

Step 420: Determine the geographic location of the target object based on the second internal reference matrix and external reference matrix of the intercepted image.

In this embodiment, the extrinsic parameter matrix acquired by the above execution subject remains unchanged, and after obtaining the second internal reference matrix of the intercepted image, the second internal reference matrix and extrinsic parameter matrix of the intercepted image can be used to compare the target in the intercepted image The object performs perceptual positioning to determine the geographic location of the target object. The geographic location may be the actual location of the target object and may be represented by coordinates.

In this implementation, the second internal reference matrix corresponding to the intercepted image is determined through the pixel focal length of the camera and the resolution of the intercepted image, and the geographic location of the target object is determined according to the second internal reference matrix and the external reference matrix, which can more accurately determine The geographic location of the target object is obtained, which improves the accuracy of location detection.

With further reference to FIG. 5 , as an implementation of the methods shown in the above figures, the present disclosure provides an embodiment of a device for determining the working distance of a camera in vehicle-road coordination. This device embodiment is similar to the method embodiment shown in FIG. 1 Correspondingly, the device can be specifically applied to various electronic devices.

As shown in FIG. 5 , the device 500 for determining the camera working distance in the vehicle-road coordination in this embodiment includes: an acquisition module 510 , an interception module 520 and a determination module 530 .

Wherein, the obtaining module 510 is configured to obtain the captured image and pixel focal length of the camera;

The intercepting module 520 is configured to intercept the intercepted image including the target object from the collected image;

The determination module 530 is configured to determine the maximum operating distance of the camera based on the pixel focal length and the number of pixels per unit distance of the intercepted image, wherein the number of pixels per unit distance of the intercepted image is included in the smallest unit that can be identified by the detection model in the intercepted image number of pixels.

In some optional ways of this embodiment, the obtaining module 510 is further configured to: obtain the physical focal length and imaging sensor parameters of the camera; determine the pixel focal length of the camera based on the physical focal length, imaging sensor parameters and the resolution of the captured image .

In some optional manners of this embodiment, the obtaining module 510 is further configured to: determine a first internal reference matrix of the camera based on the collected images and the camera calibration algorithm; obtain the pixel focal length of the camera from the first internal reference matrix.

In some optional ways of this embodiment, the number of pixels per unit distance of the intercepted image is obtained based on the following steps: obtaining the number of pixels per unit distance of the sample image in the detection model, wherein the detection model is used to detect the target object, and the unit distance of the sample image is The number of distance pixels is the number of pixels included in the smallest unit that can be recognized by the detection model in the sample image; obtain the resolution of the sample image, and determine the ratio between the resolution of the sample image and the resolution of the intercepted image; based on the sample image The number of pixels per unit distance and the scale value of , to obtain the number of pixels per unit distance of the intercepted image.

In some optional manners of this embodiment, the determination module 530 is further configured to: determine a second internal reference matrix of the intercepted image based on the resolution of the intercepted image and the first internal reference matrix; and the extrinsic matrix to determine the geographic location of the target object.

The device for determining the working distance of the camera in the vehicle-road coordination provided by the embodiments of the present disclosure obtains the captured image and the pixel focal length of the camera, and then captures the captured image including the target object from the captured image, and finally based on the pixel focal length and the unit distance of the captured image The number of pixels determines the maximum operating distance of the camera. The number of pixels per unit distance of the intercepted image is the number of pixels included in the smallest unit that can be recognized by the detection model in the intercepted image. The automatic adjustment of the camera’s operating distance can be realized by intercepting the collected image. Since the intercepted image is intercepted from the collected image, there is a certain proportional relationship between the resolution of the intercepted image and the resolution of the collected image, and the number of pixels per unit distance corresponding to the intercepted image is also the same as the number of pixels per unit distance corresponding to the collected image. The proportional relationship can use image interception to realize the change of the number of pixels per unit distance, so as to realize the change of the camera's working distance, and can increase the working distance of the camera without changing the focal length of the camera, which reduces the cost of the zoom camera and improves the adjustment of the camera. Range flexibility.

In the technical solution of the present disclosure, the acquisition, storage and application of the user's personal information involved are in compliance with relevant laws and regulations, and do not violate public order and good customs.

According to the embodiments of the present disclosure, the present disclosure also provides an electronic device, a readable storage medium, a computer program product, a roadside device, and a cloud control platform.

FIG. 6 shows a schematic block diagram of an example electronic device 600 that may be used to implement embodiments of the present disclosure. Electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are by way of example only, and are not intended to limit implementations of the disclosure described and/or claimed herein.

As shown in FIG. 6 , the electronic device 600 includes a computing unit 601, which can perform calculations according to a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. Various appropriate actions and processes are performed. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The computing unit 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604 .

Multiple components in the electronic device 600 are connected to the I/O interface 605, including: an input unit 606, such as a keyboard, a mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disk etc.; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 609 allows the device 600 to exchange information/data with other devices over a computer network such as the Internet and/or various telecommunication networks.

The computing unit 601 may be various general-purpose and/or special-purpose processing components having processing and computing capabilities. Some examples of computing units 601 include, but are not limited to, central processing units (CPUs), graphics processing units (GPUs), various dedicated artificial intelligence (AI) computing chips, various computing units that run machine learning model algorithms, digital signal processing processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 executes various methods and processes described above, for example, a method for determining the working distance of a camera in vehicle-road coordination. For example, in some embodiments, the method for determining the camera range in vehicle-road coordination can be implemented as a computer software program, which is tangibly contained in a machine-readable medium, such as the storage unit 608 . In some embodiments, part or all of the computer program may be loaded and/or installed on the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into the RAM 603 and executed by the computing unit 601, one or more steps of the method for determining the working distance of the camera in the vehicle-road coordination described above can be executed. Alternatively, in other embodiments, the calculation unit 601 may be configured in any other appropriate way (for example, by means of firmware) to execute the method for determining the working distance of the camera in the vehicle-road coordination.

Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips Implemented in a system of systems (SOC), load programmable logic device (CPLD), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include being implemented in one or more computer programs executable and/or interpreted on a programmable system including at least one programmable processor, the programmable processor Can be special-purpose or general-purpose programmable processor, can receive data and instruction from storage system, at least one input device, and at least one output device, and transmit data and instruction to this storage system, this at least one input device, and this at least one output device an output device.

Program codes for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program codes may be provided to a processor or controller of a general-purpose computer, a special purpose computer, or other programmable data processing devices, so that the program codes, when executed by the processor or controller, make the functions/functions specified in the flow diagrams and/or block diagrams Action is implemented. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package partly on the machine and partly on a remote machine or entirely on the remote machine or server.

In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media would include one or more wire-based electrical connections, portable computer discs, hard drives, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM or flash memory), optical fiber, compact disk read only memory (CD-ROM), optical storage, magnetic storage, or any suitable combination of the foregoing.

To provide for interaction with the user, the systems and techniques described herein can be implemented on a computer having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user. ); and a keyboard and pointing device (eg, a mouse or a trackball) through which a user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and can be in any form (including Acoustic input, speech input or, tactile input) to receive input from the user.

The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., as a a user computer having a graphical user interface or web browser through which a user can interact with embodiments of the systems and techniques described herein), or including such backend components, middleware components, Or any combination of front-end components in a computing system. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include: Local Area Network (LAN), Wide Area Network (WAN) and the Internet.

A computer system may include clients and servers. Clients and servers are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, a server of a distributed system, or a server combined with a blockchain.

Optionally, in addition to electronic equipment, the roadside equipment may also include communication components, etc., and the electronic equipment and communication components may be integrally integrated or separately configured. Electronic devices can obtain data from sensing devices (such as roadside cameras), such as pictures and videos, for image and video processing and data calculation. Optionally, the electronic device itself may also have a sensing data acquisition function and a communication function, such as an AI camera, and the electronic device may directly perform image and video processing and data calculation based on the acquired sensing data.

Optionally, the cloud control platform performs processing in the cloud, and the electronic devices included in the cloud control platform can obtain data from sensing devices (such as roadside cameras), such as pictures and videos, to perform image and video processing and data calculation; the cloud control platform It can also be called vehicle-road collaborative management platform, edge computing platform, cloud computing platform, central system, cloud server, etc.

It should be understood that steps may be reordered, added or deleted using the various forms of flow shown above. For example, each step described in the present disclosure may be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in the present disclosure can be achieved, no limitation is imposed herein.

The specific implementation manners described above do not limit the protection scope of the present disclosure. It should be apparent to those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made depending on design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present disclosure shall be included within the protection scope of the present disclosure.

Claims

A method for determining the working distance of a camera in vehicle-road coordination, comprising:

Obtain the captured image and pixel focal length of the camera;

intercepting an intercepted image including a target object from the acquired image;

Determine the maximum operating distance of the camera based on the pixel focal length and the number of pixels per unit distance of the intercepted image, wherein the number of pixels per unit distance of the intercepted image is the smallest unit that can be recognized by a detection model in the intercepted image The number of pixels included.
The method according to claim 1, wherein said obtaining the pixel focal length of the camera comprises:

Obtain the physical focal length and imaging sensor parameters of the camera;

A pixel focal length of the camera is determined based on the physical focal length, the imaging sensor parameters, and the resolution of the captured image.
The method according to claim 1, wherein said obtaining the pixel focal length of the camera comprises:

Determining a first internal reference matrix of the camera based on the collected image and the camera calibration algorithm;

Obtain the pixel focal length of the camera from the first internal reference matrix.
The method according to claim 1, wherein the number of pixels per unit distance of the intercepted image is obtained based on the following steps:

Obtain the number of pixels per unit distance of the sample image in the detection model, wherein the detection model is used to detect the target object, and the number of pixels per unit distance of the sample image is the number of pixels in the sample image that can be recognized by the detection model The number of pixels included in the smallest unit of ;

Acquiring the resolution of the sample image, and determining a ratio between the resolution of the sample image and the resolution of the intercepted image;

Based on the number of pixels per unit distance of the sample image and the ratio value, the number of pixels per unit distance of the intercepted image is acquired.
The method according to claim 3, wherein the method further comprises:

determining a second internal reference matrix corresponding to the intercepted image based on the resolution of the intercepted image and the first internal reference matrix;

Based on the second internal parameter matrix and external parameter matrix corresponding to the intercepted image, the geographic location of the target object is determined.
A camera action distance determination device in vehicle-road coordination, comprising:

An acquisition module configured to acquire the captured image and pixel focal length of the camera;

an intercepting module configured to intercept an intercepted image including a target object from the acquired image;

The determination module is configured to determine the maximum operating distance of the camera based on the pixel focal length and the number of pixels per unit distance of the intercepted image, wherein the number of pixels per unit distance of the intercepted image is the number of pixels per unit distance in the intercepted image that can be The number of pixels included in the smallest unit recognized by the detection model.
The device according to claim 6, wherein the acquisition module is further configured to:

Obtain the physical focal length and imaging sensor parameters of the camera;

A pixel focal length of the camera is determined based on the physical focal length, the imaging sensor parameters, and the resolution of the captured image.
The device according to claim 6, wherein the acquisition module is further configured to:

Determining a first internal reference matrix of the camera based on the collected image and the camera calibration algorithm;

Obtain the pixel focal length of the camera from the first internal reference matrix.
The device according to claim 6, wherein the number of pixels per unit distance of the intercepted image is obtained based on the following steps:

Obtain the number of pixels per unit distance of the sample image in the detection model, wherein the detection model is used to detect the target object, and the number of pixels per unit distance of the sample image is the number of pixels in the sample image that can be recognized by the detection model The number of pixels included in the smallest unit of ;

Acquiring the resolution of the sample image, and determining a ratio between the resolution of the sample image and the resolution of the intercepted image;

Based on the number of pixels per unit distance of the sample image and the ratio value, the number of pixels per unit distance of the intercepted image is acquired.
The device according to claim 8, wherein the determining module is further configured to:

determining a second internal reference matrix corresponding to the intercepted image based on the resolution of the intercepted image and the first internal reference matrix;

Based on the second internal parameter matrix and external parameter matrix corresponding to the intercepted image, the geographic location of the target object is determined.
An electronic device comprising:

at least one processor; and

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can perform any one of claims 1-5. Methods.
A non-transitory computer-readable storage medium storing computer instructions, wherein the computer instructions are used to cause a computer to execute the method according to any one of claims 1-5.
A computer program product comprising a computer program which, when executed by a processor, implements the method according to any one of claims 1-5.
A roadside device, comprising the electronic device according to claim 11.
A cloud control platform, comprising the electronic device according to claim 11.