Camera calibration method, device and system and storage medium

Abstract

The invention discloses a camera calibration method, device and system and a storage medium. The method comprises the following steps: judging whether a video capture object reads a video image of at least part of a camera or not; when a video capture object reads the video images of the at least part of the cameras, executing image acquisition operation on the cameras which read the video images to obtain corresponding camera view images; performing image identification on the camera view image to identify whether a preset target exists in the camera view image; when the number of preset targets in the camera view image is smaller than a preset number and the area proportion of the preset targets occupying the lane is smaller than a preset proportion, determining the camera view image as an original view image corresponding to the camera; and taking the original view image corresponding to the camera as a camera calibration image to complete the calibration operation of the camera. By adopting the scheme, the camera calibration efficiency is improved, and the time consumption in a large-batch camera calibration scene is saved.

Description

Technical Field

[0001] The present application relates to the field of camera calibration technology, and in particular to a camera calibration method, device, system and storage medium. Background Art

[0002] In some image acquisition scenarios, multiple cameras at fixed locations are required for image acquisition operations. For example, in the autonomous driving industry, road-side equipment connected to the cloud platform needs to be calibrated when deployed. During camera calibration, the original field of view image acquisition is required for each camera field of view.

[0003] In the prior art, the original field of view image acquisition work is done manually. When manually acquiring the original field of view image, the staff needs to log in to the management background of each camera one by one, screen and identify the video in the camera frame by frame, and select images without preset targets (such as people and vehicles) or with fewer preset targets as the original field of view images. This method not only requires a lot of manpower costs, but also has a long screening cycle and low camera calibration efficiency. If a large number of cameras need to be calibrated, it will take a lot of time.

[0004] Therefore, how to provide a camera calibration method to save labor costs, improve the efficiency of camera calibration, and save time consumption in large-scale camera calibration scenarios is a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a camera calibration method to improve the efficiency of camera calibration and save time consumption in large-scale camera calibration scenarios, including:

[0006] When the RTSP connection for pulling streams with at least some of the cameras to be deployed in the preset list is successful, determining whether the video capture object has read the video images of at least some of the cameras;

[0007] When the video capture object reads the video image of at least part of the camera, performing an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image;

[0008] Performing image recognition on the camera field of view image to identify whether a preset target exists in the camera field of view image;

[0009] When the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio, determining that the camera field of view image is an original field of view image corresponding to the camera;

[0010] The original field of view image corresponding to the camera is used as the camera calibration image to complete the calibration operation of the camera.

[0011] The beneficial effects of the present application are that it can automatically connect to the RTSP streaming of multiple cameras, and can perform image acquisition and recognition on cameras that can read video images, so as to determine whether there are preset targets in the camera field of view image, and automatically screen out camera field of view images in which the number of preset targets is less than the preset number and the area ratio of the lane occupied by the preset targets is less than the preset ratio as the original field of view images for camera calibration operations, thereby realizing automatic calibration operations of the camera, saving labor costs, and being able to recognize multiple camera field of view images at the same time, improving the screening efficiency of the camera field of view images, thereby improving the efficiency of camera calibration, and saving time consumption in large-scale camera calibration scenarios.

[0012] In one embodiment, performing an image acquisition operation on a camera that reads a video image to obtain a corresponding camera field of view image includes:

[0013] Intercepting video images of all cameras to be deployed in a preset list according to a preset time interval;

[0014] Generate a corresponding camera field of view image based on the captured video image.

[0015] In one embodiment, the method further comprises:

[0016] Concurrently connect to the RTSP addresses of all cameras and read the video images of all cameras based on the video capture object.

[0017] In one embodiment, the method further comprises:

[0018] Obtain first preset information of all cameras to be deployed from the preset list;

[0019] The RTSP addresses of all camera streams are determined according to the first preset information.

[0020] In one embodiment, the process of establishing the preset list is as follows:

[0021] Obtain second preset information of the camera to be deployed, wherein the second preset information includes at least one of the following information:

[0022] Location, orientation, internal parameters, external parameters, IP address, camera username and camera password;

[0023] The second preset information is stored in a preset list.

[0024] In one embodiment, performing image recognition on the camera field of view image to identify whether there is a preset target in the camera field of view image includes:

[0025] Identify whether there is a target of a preset shape in the camera field of view image;

[0026] When there is an object of a preset shape, it is determined that there is the preset object in the camera field of view image.

[0027] In one embodiment, the method further comprises:

[0028] When the number of preset targets in the camera field of view image is greater than the preset number, the image acquisition operation of the camera is continued to be performed until the original field of view image corresponding to the camera is determined, and then the image acquisition operation of the camera is stopped.

[0029] The present application also provides a camera calibration device, comprising:

[0030] A judging module, used for judging whether the video capture object has read the video images of at least some of the cameras in the preset list when the RTSP connection for pulling the stream with at least some of the cameras to be deployed is successful;

[0031] A first acquisition module, configured to, when the video capture object reads the video image of at least part of the camera, perform an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image;

[0032] A recognition module, used to perform image recognition on the camera field of view image to identify whether there is a preset target in the camera field of view image;

[0033] A first determination module, configured to determine that the camera field of view image is an original field of view image corresponding to the camera when the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio;

[0034] The calibration module is used to complete the calibration operation of the camera by using the original field of view image corresponding to the camera as the camera calibration image.

[0035] In one embodiment, the acquisition module includes:

[0036] An interception submodule is used to intercept the video images of all the cameras to be deployed in the preset list according to a preset time interval;

[0037] The generation submodule is used to generate a corresponding camera field of view image according to the captured video image.

[0038] In one embodiment, the apparatus further comprises:

[0039] The connection module is used to concurrently connect to the RTSP addresses of all camera streams and read the video images of all cameras according to the video capture object.

[0040] In one embodiment, the apparatus further comprises:

[0041] An acquisition module, used for acquiring first preset information of all cameras to be deployed from a preset list;

[0042] The second determining module is used to determine the RTSP addresses of all camera streams according to the first preset information.

[0043] In one embodiment, the process of establishing the preset list is as follows:

[0044] Obtain second preset information of the camera to be deployed, wherein the second preset information includes at least one of the following information:

[0045] Location, orientation, internal parameters, external parameters, IP address, camera username and camera password;

[0046] The second preset information is stored in a preset list.

[0047] In one embodiment, the identification module includes:

[0048] A recognition submodule, used to recognize whether there is a target of a preset shape in the camera field of view image;

[0049] The determination submodule is used to determine whether a preset target exists in the camera field of view image when a target of a preset shape exists.

[0050] In one embodiment, the apparatus further comprises:

[0051] The second acquisition module is used to continue the image acquisition operation of the camera when the number of preset targets in the camera field of view image is greater than the preset number, and stop the image acquisition operation of the camera until the original field of view image corresponding to the camera is determined.

[0052] The present application also provides a camera calibration system, comprising:

[0053] at least one processor; and,

[0054] a memory communicatively connected to the at least one processor; wherein,

[0055] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the camera calibration method recorded in any one of the above embodiments.

[0056] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the camera calibration system, the camera calibration system can implement the camera calibration method recorded in any one of the above embodiments.

[0057] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings.

[0058] The technical solution of the present application is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] The accompanying drawings are used to provide a further understanding of the present application and constitute a part of the specification. Together with the embodiments of the present application, they are used to explain the present application and do not constitute a limitation of the present application. In the accompanying drawings:

[0060] Figure 1 This is a flow chart of a camera calibration method in one embodiment of the present application;

[0061] Figure 2 This is a block diagram of a camera calibration device in one embodiment of the present application;

[0062] Figure 3 Schematic diagram of the hardware structure of a camera calibration system in one embodiment of the present application. DETAILED DESCRIPTION

[0063] The preferred embodiments of the present application are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application and are not used to limit the present application.

[0064] Figure 1 Flow chart of a camera calibration method in one embodiment of the present application, which can be used to improve the efficiency of camera calibration and save time consumption in large-scale camera calibration scenarios, such as Figure 1 As shown, the method can be implemented as the following steps S101-S105:

[0065] In step S101, when the RTSP connection for pulling streams with at least some of all the cameras to be deployed in the preset list is successful, it is determined whether the video capture object has read the video images of at least some of the cameras;

[0066] In step S102, when the video capture object reads the video image of at least part of the camera, an image acquisition operation is performed on the camera that reads the video image to obtain a corresponding camera field of view image;

[0067] In step S103, image recognition is performed on the camera field of view image to identify whether there is a preset target in the camera field of view image;

[0068] In step S104, when the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio, the camera field of view image is determined to be an original field of view image corresponding to the camera;

[0069] In step S105, the original field of view image corresponding to the camera is used as the camera calibration image to complete the calibration operation of the camera.

[0070] This application is mainly used in camera deployment scenarios, for example, it can be camera deployment of road-side equipment in the field of autonomous driving. First, the second preset information of the camera to be deployed can be obtained, wherein the second preset information includes at least one of the following information: location, orientation, internal parameter, external parameter, IP address, camera user name and camera password; the second preset information is stored in a preset list. The preset list can be in Excel format.

[0071] When executing this application, first parse the preset list, obtain the first preset information of all cameras to be deployed from the preset list; then determine the rtsp address of the camera stream according to the first preset information (such as each camera's ip address, camera's user name, and camera's password). Calculate the number of image acquisitions and the interval duration of each image acquisition according to the filled-in camera image acquisition frequency and camera acquisition duration.

[0072] Concurrently connect to the RTSP addresses of all camera streams, and read the video images of all cameras according to the video capture object (for example, videoCapture object).

[0073] When the RTSP connection for pulling streams with at least some of the cameras to be deployed in the preset list is successful, determining whether the video capture object has read the video images of at least some of the cameras;

[0074] When the video capture object reads the video image of at least part of the camera, an image acquisition operation is performed on the camera that reads the video image to obtain a corresponding camera field of view image; specifically, the image acquisition operation is performed on the camera that reads the video image to obtain the corresponding camera field of view image in the following manner:

[0075] The video images of all the cameras to be deployed in the preset list are intercepted according to the preset time interval; and the corresponding camera field of view images are generated according to the intercepted video images.

[0076] Performing image recognition on the camera field of view image to identify whether a preset target exists in the camera field of view image;

[0077] Specifically, it is identified whether there is a target of a preset shape in the camera field of view image; when there is a target of the preset shape, it is determined that there is the preset target in the camera field of view image.

[0078] When the number of preset targets in the camera field of view image is less than the preset number and the area ratio of the lane occupied by the preset targets is less than the preset ratio, the camera field of view image is determined to be the original field of view image corresponding to the camera; wherein the area ratio of the lane occupied by the preset targets is calculated as follows:

[0079] For example, when the preset target is a person or a car, when the preset target is identified, a rectangular bounding box that encloses the preset target is usually generated. The bounding box consists of four values, the x and y coordinates of the upper left corner and the width and height). The area (width * height) occupied by each target in the field of view image can be calculated through the pixel position information, and then the area occupied by the lane is calculated in combination with the high-precision map (for example, the high-precision map can be projected onto the image point first, and then the high-precision map is filled with a color, and then the area occupied by the filled color is detected. The area is the corresponding lane area), and then the area ratio of the lane occupied by the recognized preset target is calculated (based on the target area calculated above / the area calculated by the lane).

[0080] When the number of preset targets in the camera field of view image is greater than the preset number, the image acquisition operation of the camera is continued to be performed until the original field of view image corresponding to the camera is determined, and then the image acquisition operation of the camera is stopped.

[0081] The original field of view image corresponding to the camera is used as the camera calibration image to complete the calibration operation of the camera.

[0082] In this application, a collection number threshold can be set. When the system collection number reaches this threshold, if a suitable original field of view image is still not found. When this collection number threshold is set to a large value, it may be due to some other reasons that a suitable original field of view image cannot be found. At this time, the collection can be stopped. This method is firstly to facilitate manual intervention to see whether the original field of view image cannot be collected due to other reasons. In addition, it is also to prevent the inability to find a suitable original field of view image due to other reasons, thereby causing a waste of resources.

[0083] The beneficial effects of the present application are that it can automatically connect to the RTSP streaming of multiple cameras, and can perform image acquisition and recognition on cameras that can read video images, so as to determine whether there are preset targets in the camera field of view image, and automatically screen out camera field of view images in which the number of preset targets is less than the preset number and the area ratio of the lane occupied by the preset targets is less than the preset ratio as the original field of view images for camera calibration operations, thereby realizing automatic calibration operations of the camera, saving labor costs, and being able to recognize multiple camera field of view images at the same time, improving the screening efficiency of the camera field of view images, thereby improving the efficiency of camera calibration, and saving time consumption in large-scale camera calibration scenarios.

[0084] In one embodiment, performing an image acquisition operation on a camera that reads a video image to obtain a corresponding camera field of view image includes:

[0085] Intercepting video images of all cameras to be deployed in a preset list according to a preset time interval;

[0086] Generate a corresponding camera field of view image based on the captured video image.

[0087] In one embodiment, the method further comprises:

[0088] Concurrently connect to the RTSP addresses of all cameras and read the video images of all cameras based on the video capture object.

[0089] In one embodiment, the method further comprises:

[0090] Obtain first preset information of all cameras to be deployed from the preset list;

[0091] The RTSP addresses of all camera streams are determined according to the first preset information.

[0092] In one embodiment, the process of establishing the preset list is as follows:

[0093] Obtain second preset information of the camera to be deployed, wherein the second preset information includes at least one of the following information:

[0094] Location, orientation, internal parameters, external parameters, IP address, camera username and camera password;

[0095] The second preset information is stored in a preset list.

[0096] In one embodiment, performing image recognition on the camera field of view image to identify whether there is a preset target in the camera field of view image includes:

[0097] Identify whether there is a target of a preset shape in the camera field of view image;

[0098] When there is an object of a preset shape, it is determined that there is the preset object in the camera field of view image.

[0099] In one embodiment, the method further comprises:

[0100] When the number of preset targets in the camera field of view image is greater than the preset number, the image acquisition operation of the camera is continued to be performed until the original field of view image corresponding to the camera is determined, and then the image acquisition operation of the camera is stopped.

[0101] In a general embodiment, a camera calibration method provided by the present application can be implemented as follows:

[0102] First, you need to prepare the point information of the deployed cameras and manage the point information of each camera in the form of Excel. Excel mainly contains the IP address of the camera and the username and password for the camera backend connection. After importing the camera point information Excel file, you can fill in the frequency of each point acquisition and the total acquisition time. Then automatically collect and filter the field of view images of each camera point.

[0103] The calculation logic of the overall device tool is as follows:

[0104] (1) First parse the Excel file of the camera point information, and then determine the RTSP address of the camera stream according to each camera IP, camera username, and camera password. Then calculate the number of image acquisitions and the interval duration of each image acquisition according to the filled-in camera image acquisition frequency and camera acquisition duration.

[0105] (2) Then you can connect to multiple rtsp addresses in parallel, create a videoCapture object to read each frame of the video stream, if the rtsp connection is successful and each frame of the image is read, then enter the camera image acquisition process. Then, according to the interval length of the image acquisition, intercept the real-time video stream that is pulled in real time to generate the corresponding camera field of view image. In the process of collecting images, use image recognition to calculate the preset target on the image (mainly based on the current yo lo target set training and preset target recognition). The preset target is mainly for motor vehicles and non-motor vehicles. Target recognition can generate relevant information such as the pixel position of the target in the camera field of view (the position and size of the preset target in the image, the bounding box consists of four values, the x and y coordinates of the upper left corner and the width and height). The pixel position information can be used to calculate the area (width * height) occupied by each preset target in the field of view image. Then, combine the high-precision map to calculate the area occupied by the lane (you can first project the high-precision map onto the image point, then fill the high-precision map with a color, and then detect the area occupied by the filled color to be the corresponding lane area). Then calculate the area ratio of the lane occupied by the identified preset target (according to the above calculated target area / the area calculated by the lane). When the number of preset targets in the camera field of view image is less than the preset number and the area ratio of the lane occupied by the preset targets is less than the preset ratio, determine that the camera field of view image is the original field of view image corresponding to the camera.

[0106] Through the above method, the corresponding folders can be generated automatically according to the IP of the camera to store the filtered captured field of view images. This can not only automate the management of the collected data, but also reduce the manpower to log in to the camera management backend one by one to filter and screenshot the images with few or no targets in the camera. It greatly reduces the manpower cost and time cost. It lays the foundation for the subsequent calibration work. It greatly improves the overall efficiency of large-scale point camera calibration.

[0107] Figure 2 A block diagram of a camera calibration device for this application is shown in FIG. Figure 2 As shown, including:

[0108] A judgment module 201 is used to judge whether the video capture object has read the video images of at least some of the cameras in the preset list when the RTSP connection for pulling the stream with at least some of the cameras to be deployed is successful;

[0109] A first acquisition module 202 is configured to, when the video capture object reads the video image of at least part of the camera, perform an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image;

[0110] The recognition module 203 is used to perform image recognition on the camera field of view image to identify whether there is a preset target in the camera field of view image;

[0111] A first determination module 204 is used to determine that the camera field of view image is an original field of view image corresponding to the camera when the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio;

[0112] The calibration module 205 is used to complete the calibration operation of the camera by using the original field of view image corresponding to the camera as the camera calibration image.

[0113] In one embodiment, the acquisition module includes:

[0114] An interception submodule is used to intercept the video images of all the cameras to be deployed in the preset list according to a preset time interval;

[0115] The generation submodule is used to generate a corresponding camera field of view image according to the captured video image.

[0116] In one embodiment, the apparatus further comprises:

[0117] The connection module is used to concurrently connect to the RTSP addresses of all camera streams and read the video images of all cameras according to the video capture object.

[0118] In one embodiment, the apparatus further comprises:

[0119] An acquisition module, used for acquiring first preset information of all cameras to be deployed from a preset list;

[0120] The second determining module is used to determine the RTSP addresses of all camera streams according to the first preset information.

[0121] In one embodiment, the process of establishing the preset list is as follows:

[0122] Obtain second preset information of the camera to be deployed, wherein the second preset information includes at least one of the following information:

[0123] Location, orientation, internal parameters, external parameters, IP address, camera username and camera password;

[0124] The second preset information is stored in a preset list.

[0125] In one embodiment, the identification module includes:

[0126] A recognition submodule, used to recognize whether there is a target of a preset shape in the camera field of view image;

[0127] The determination submodule is used to determine whether a preset target exists in the camera field of view image when a target of a preset shape exists.

[0128] In one embodiment, the apparatus further comprises:

[0129] The second acquisition module is used to continue the image acquisition operation of the camera when the number of preset targets in the camera field of view image is greater than the preset number, and stop the image acquisition operation of the camera until the original field of view image corresponding to the camera is determined.

[0130] The present application also provides a camera calibration system, comprising:

[0131] at least one processor; and,

[0132] a memory communicatively connected to the at least one processor; wherein,

[0133] The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the camera calibration method recorded in any one of the above embodiments.

[0134] Figure 3 FIG. 1 is a schematic diagram of the hardware structure of a camera calibration system in one embodiment of the present application. Figure 3 As shown, the camera calibration system includes:

[0135] at least one processor 320; and,

[0136] A memory 304 in communication with the at least one processor 320; wherein,

[0137] The memory 304 stores instructions that can be executed by the at least one processor 320, and the instructions are executed by the at least one processor 320 to implement the camera calibration method described in any of the above embodiments.

[0138] Reference Figure 3 The camera calibration system 300 may include one or more of the following components: a processing component 302 , a memory 304 , a power component 306 , a multimedia component 308 , an audio component 310 , an input / output (I / O) interface 312 , a sensor component 314 , and a communication component 316 .

[0139] The processing component 302 generally controls the overall operation of the camera calibration system 300. The processing component 302 may include one or more processors 320 to execute instructions to complete all or part of the steps of the above method. In addition, the processing component 302 may include one or more modules to facilitate the interaction between the processing component 302 and other components. For example, the processing component 302 may include a multimedia module to facilitate the interaction between the multimedia component 308 and the processing component 302.

[0140] The memory 304 is configured to store various types of data to support the operation of the camera calibration system 300. Examples of such data include instructions for any application or method operating on the camera calibration system 300, such as text, pictures, videos, etc. The memory 304 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0141] The power supply component 306 provides power to various components of the camera calibration system 300. The power supply component 306 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the camera calibration system 300.

[0142] The multimedia component 308 includes a screen that provides an output interface between the camera calibration system 300 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touch screen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touch, slide, and gestures on the touch panel. The touch sensor may not only sense the boundaries of the touch or slide action, but also detect the duration and pressure associated with the touch or slide operation. In some embodiments, the multimedia component 308 may also include a front camera and / or a rear camera. When the camera calibration system 300 is in an operating mode, such as a shooting mode or a video mode, the front camera and / or the rear camera may receive external multimedia data. Each front camera and rear camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0143] The audio component 310 is configured to output and / or input audio signals. For example, the audio component 310 includes a microphone (MIC), and when the camera calibration system 300 is in an operation mode, such as an alarm mode, a recording mode, a voice recognition mode, and a voice output mode, the microphone is configured to receive an external audio signal. The received audio signal can be further stored in the memory 304 or sent via the communication component 316. In some embodiments, the audio component 310 also includes a speaker for outputting audio signals.

[0144] I / O interface 312 provides an interface between processing component 302 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include but are not limited to: a home button, a volume button, a start button, and a lock button.

[0145] The sensor assembly 314 includes one or more sensors for providing various aspects of status assessment for the camera calibration system 300. For example, the sensor assembly 314 may include a sound sensor. In addition, the sensor assembly 314 may detect the open / closed state of the camera calibration system 300, the relative positioning of the components, such as the display and keypad of the camera calibration system 300, and the sensor assembly 314 may also detect the operating state of the camera calibration system 300 or a component of the camera calibration system 300, such as the operating state of the air distribution plate, the structural state, the operating state of the discharge scraper, etc., the orientation or acceleration / deceleration of the camera calibration system 300 and the temperature change of the camera calibration system 300. The sensor assembly 314 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. The sensor assembly 314 may also include an optical sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, the sensor assembly 314 may also include an accelerometer, a gyroscope sensor, a magnetic sensor, a pressure sensor, a material stack thickness sensor, or a temperature sensor.

[0146] The communication component 316 is configured to enable the camera calibration system 300 to provide the ability to communicate with other devices and cloud platforms in a wired or wireless manner. The camera calibration system 300 can access a wireless network based on a communication standard, such as WiFi, 2G or 3G, or a combination thereof. In an exemplary embodiment, the communication component 316 receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component 316 also includes a near field communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0147] In an exemplary embodiment, the camera calibration system 300 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to execute the camera calibration method described in any of the above embodiments.

[0148] The present application also provides a computer-readable storage medium. When the instructions in the storage medium are executed by a processor corresponding to the camera calibration system, the camera calibration system can implement the camera calibration method recorded in any one of the above embodiments.

[0149] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0150] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0151] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0152] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0153] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A camera calibration method, characterized in that: include: When the RTSP connection for pulling streams with at least some of all the cameras to be deployed in the preset list is successful, determining whether the video capture object has read the video images of at least some of the cameras; When the video capture object reads the video image of at least part of the camera, performing an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image; Performing image recognition on the camera field of view image to identify whether a preset target exists in the camera field of view image; When the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio, determining that the camera field of view image is an original field of view image corresponding to the camera; The original field of view image corresponding to the camera is used as the camera calibration image to complete the calibration operation of the camera.

2. The method according to claim 1, characterized in that The performing an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image includes: Intercepting video images of all cameras to be deployed in a preset list according to a preset time interval; Generate a corresponding camera field of view image based on the captured video image.

3. The method according to claim 1, characterized in that The method further comprises: Concurrently connect to the RTSP addresses of all cameras and read the video images of all cameras based on the video capture object.

4. The method according to claim 1, characterized in that The method further comprises: Obtain first preset information of all cameras to be deployed from the preset list; The RTSP addresses of all camera streams are determined according to the first preset information.

5. The method according to claim 1, characterized in that The process of establishing the preset list is as follows: Obtain second preset information of the camera to be deployed, wherein the second preset information includes at least one of the following information: Location, orientation, internal parameters, external parameters, IP address, camera username and camera password; The second preset information is stored in a preset list.

6. The method according to claim 1, characterized in that Performing image recognition on the camera field of view image to identify whether a preset target exists in the camera field of view image, including: Identify whether there is a target of a preset shape in the camera field of view image; When there is an object of a preset shape, it is determined that there is the preset object in the camera field of view image.

7. The method according to claim 1, characterized in that The method further comprises: When the number of preset targets in the camera field of view image is greater than the preset number, the image acquisition operation of the camera is continued to be performed until the original field of view image corresponding to the camera is determined, and then the image acquisition operation of the camera is stopped.

8. A camera calibration device, characterized in that: include: A judging module, used for judging whether the video capture object has read the video images of at least some of the cameras in the preset list when the RTSP connection for pulling the stream with at least some of the cameras to be deployed is successful; A first acquisition module, configured to, when the video capture object reads the video image of at least part of the camera, perform an image acquisition operation on the camera that reads the video image to obtain a corresponding camera field of view image; A recognition module, used to perform image recognition on the camera field of view image to identify whether there is a preset target in the camera field of view image; A first determination module, configured to determine that the camera field of view image is an original field of view image corresponding to the camera when the number of preset targets in the camera field of view image is less than a preset number and the area ratio of the lane occupied by the preset targets is less than a preset ratio; The calibration module is used to complete the calibration operation of the camera by using the original field of view image corresponding to the camera as the camera calibration image.

9. A camera calibration system, characterized in that: include: at least one processor; as well as, a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to implement the camera calibration method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: When the instructions in the storage medium are executed by a processor corresponding to the camera calibration system, the camera calibration system can implement the camera calibration method according to any one of claims 1 to 7.

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