CN113824949A - Video quality diagnosis method, equipment and storage medium for camera - Google Patents
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Abstract
The invention discloses a video quality diagnosis method, equipment and a storage medium of a camera, belonging to the field of video monitoring/video management. The method comprises the following steps: acquiring a first video file recorded by a camera to be tested and a second video file recorded by an unmanned aerial vehicle camera, wherein the first video file and the second video file are respectively obtained by recording the camera to be tested and the unmanned aerial vehicle camera under the same shooting condition; respectively acquiring a first image and a second image within a preset time range from the first video file and the second video file; and determining a video quality diagnosis result of the camera to be detected by performing feature analysis on the first image and the second image. According to the technical scheme, the accuracy of video quality diagnosis can be improved under the conditions that the accuracy of the current video quality diagnosis algorithm is not optimized and a common camera is not additionally modified.
Description
Technical Field
The invention relates to the field of video monitoring/video management, in particular to a video quality diagnosis method, video quality diagnosis equipment and a storage medium for a video camera.
Background
A large number of cameras are connected into a video management platform, and how to judge whether images of the cameras are abnormal is a problem encountered by the video management platform in the operation process. In the prior art, the algorithm characteristic analysis is directly carried out on a camera picture, a video management platform submits a video code stream or a picture of a camera needing to be analyzed to a quality diagnosis module, the quality diagnosis module carries out the characteristic analysis on the picture through an algorithm, and problems (such as snowflake, rolling screen, blurring, color cast and the like) corresponding to the algorithm can be analyzed by adopting different algorithms.
However, the accuracy of the diagnosis result determined by each algorithm is low at present, and false alarm often occurs.
Disclosure of Invention
The invention provides a video quality diagnosis method, equipment and a storage medium of a camera, and aims to improve the accuracy of video quality diagnosis.
In order to achieve the above object, an embodiment of the present invention provides a camera video quality diagnosis method, including the following steps: acquiring a first video file recorded by a camera to be tested and a second video file recorded by an unmanned aerial vehicle camera, wherein the first video file and the second video file are respectively obtained by recording the camera to be tested and the unmanned aerial vehicle camera under the same shooting condition; respectively acquiring a first image and a second image within a preset time range from the first video file and the second video file; and determining a video quality diagnosis result of the camera to be detected by performing feature analysis on the first image and the second image.
In order to achieve the above object, an embodiment of the present invention further provides a camera video quality diagnosis apparatus, which includes a memory, a processor, a program stored in the memory and executable on the processor, and a data bus for implementing connection communication between the processor and the memory, wherein the program, when executed by the processor, implements the steps of the camera video quality diagnosis method.
To achieve the above object, the present invention provides a storage medium for a computer-readable storage, the storage medium storing one or more programs, the one or more programs being executable by one or more processors to implement the aforementioned steps of the camera video quality diagnosis method.
The camera video quality diagnosis method, the equipment and the storage medium provided by the embodiment of the invention take the second image shot by the unmanned aerial vehicle camera in the same shooting environment as the camera to be detected as a standard, analyze and compare the first image shot by the camera to be detected with the second image shot by the unmanned aerial vehicle camera, diagnose the video quality of the camera to be detected, improve the accuracy of video quality diagnosis and reduce or eliminate false alarm caused by low correctness of the diagnosis result of the existing video quality diagnosis algorithm under the conditions of not optimizing the existing video quality diagnosis algorithm and not additionally transforming a common camera.
Drawings
Fig. 1 is a detailed flow chart of camera video quality diagnostics according to an embodiment of the present invention.
Fig. 2 is a diagram of a conventional system model of a video management platform.
Fig. 3 is a schematic structural diagram of a video management platform system according to an embodiment of the present invention.
Fig. 4 is a detailed flow chart of the system of fig. 3 implementing the diagnostic process.
Fig. 5 is a block diagram of a video quality diagnosis apparatus of a camera according to an embodiment of the present invention.
Detailed Description
It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the following description, suffixes such as "module", "part", or "unit" used to denote elements are used only for facilitating the explanation of the present invention, and have no peculiar meaning in itself. Thus, "module", "component" or "unit" may be used mixedly.
As shown in fig. 1, an embodiment of the present invention provides a camera video quality diagnosis method, which may include the following steps, as shown in fig. 1:
step S200: the method comprises the steps of obtaining a first video file recorded by a camera to be detected and a second video file recorded by an unmanned aerial vehicle camera, wherein the first video file and the second video file are obtained by recording the camera to be detected and the unmanned aerial vehicle camera under the same shooting condition respectively. Specifically, the step 200 may include: the camera video quality diagnosis equipment firstly sends a video recording instruction to the camera to be detected and the unmanned aerial vehicle camera, so that the camera to be detected and the unmanned aerial vehicle camera record the first video file and the second video file respectively according to the video recording instruction, and then receives the first video file sent by the camera to be detected and the second video file sent by the unmanned aerial vehicle camera.
Wherein the same photographing condition may include: the unmanned aerial vehicle camera hovers near the camera to be detected, for example, the unmanned aerial vehicle camera is arranged side by side up and down or arranged side by side left and right with the camera to be detected, and the orientation of the unmanned aerial vehicle camera is consistent with that of the camera to be detected; the same photographing condition may further include: the focal length of the unmanned aerial vehicle camera is consistent with that of the camera to be detected. Therefore, the unmanned aerial vehicle camera can shoot the shooting object which is the same as that of the camera to be detected, and the shot image is used as the diagnostic standard of the video quality of the camera to be detected.
Step S400: and respectively acquiring a first image and a second image within a preset time range from the first video file and the second video file.
The predetermined time range may be a short time range, e.g. within 10s, within 5s, for improved diagnostic accuracy, preferably the first and second images at the same point in time.
Step S600: and determining a video quality diagnosis result of the camera to be detected by performing feature analysis on the first image and the second image.
The camera video quality diagnosis equipment respectively carries out feature analysis on the first image and the second image by utilizing a first video quality diagnosis algorithm to obtain a first image feature value and a second image feature value, and determines a video quality diagnosis result of the camera to be detected according to a difference value of the first image feature value and the second image feature value, for example, if the difference value of the first image feature value and the second image feature value is smaller than a preset range, the video quality of the camera to be detected is determined to meet the requirement.
In order to improve the camera video quality diagnosis accuracy, the present embodiment may further adjust the drone camera before executing step S200.
It is right the unmanned aerial vehicle camera is adjusted and can be included unmanned aerial vehicle camera position and orientation adjustment, specifically is: acquiring the position information and the camera orientation information of the camera to be detected; and sending a moving instruction carrying the position information and the orientation information of the camera to the unmanned aerial vehicle camera, so that the unmanned aerial vehicle camera moves to a position (for example, up, down, left and right side by side) near the camera to be detected to hover according to the position information in the moving instruction, and adjusting the orientation of a camera holder to be consistent with the orientation of the camera to be detected according to the orientation information of the camera in the moving instruction.
The adjustment of the unmanned aerial vehicle camera can also comprise the adjustment of the focal length of the unmanned aerial vehicle camera, and the adjustment modes are various, for example, the focal length of the unmanned aerial vehicle camera can be adjusted in advance; the method can also be adjusted in the process that the unmanned aerial vehicle camera moves to the camera to be detected, specifically, the focal length information of the camera to be detected is obtained, and a moving instruction carrying the focal length information is sent to the unmanned aerial vehicle camera, so that the unmanned aerial vehicle camera adjusts the focal length according to the focal length information in the moving instruction; the focal length can be adjusted according to the image obtained by shooting the same object by the unmanned aerial vehicle camera and the camera to be detected after the unmanned aerial vehicle camera is in place.
It is right the unmanned aerial vehicle camera is adjusted and can also include the camera cloud platform adjustment of unmanned aerial vehicle camera, specifically is: after a moving instruction carrying the position information and the camera orientation information is sent to the unmanned aerial vehicle camera, receiving an arrival message sent after the unmanned aerial vehicle camera moves to the position near the position of the camera to be detected and has the same orientation with the camera to be detected; after receiving the arrival message, adjusting a camera pan-tilt of the unmanned aerial vehicle camera to a specified position, specifically, sending a snapshot instruction to the camera to be tested and the unmanned aerial vehicle camera; receiving a first snapshot image shot by the camera to be detected according to the snapshot instruction, a plurality of second snapshot images shot by the unmanned aerial vehicle camera according to the snapshot instruction and a plurality of corresponding cradle head absolute positions, wherein each second snapshot image is obtained by shooting a camera cradle head of the unmanned aerial vehicle camera at different cradle head absolute positions; respectively determining the similarity between the first snapshot image and each second snapshot image, acquiring the cradle head absolute position corresponding to any second snapshot image with the similarity within the similarity threshold range, and taking the cradle head absolute position as a designated position; and sending a moving instruction carrying the designated position information of the camera holder to the unmanned aerial vehicle camera, so that the camera holder of the unmanned aerial vehicle camera moves to the designated position, and the camera holder records the second video file at the designated position.
Wherein the determining the similarity of the first captured image and each of the second captured images respectively may include: and respectively comparing the characteristics of the first snapshot image with each second snapshot image to determine the similarity of the first snapshot image and each second snapshot image.
By the embodiment of the invention, the accuracy of video quality diagnosis can be improved without optimizing the current video quality diagnosis algorithm and additionally modifying a common camera, the implementation cost is low, and the management and maintenance capability of a video management platform on the camera can be improved.
Taking a video management platform in a security system as an example, as shown in fig. 2, a conventional security management platform and a corresponding video quality diagnosis system mainly include: the system comprises a management module, a storage module, a service processing module, an access module, a streaming media capability module and a video quality diagnosis module. The accuracy of the diagnosis of the implementation mode is determined by introducing a video quality diagnosis module, more specifically, a diagnosis algorithm of the video quality diagnosis module, and the improvement of the accuracy of the algorithm is difficult. In addition, the correctness of the diagnosis result of the current algorithm is not high enough, and the false alarm condition is common.
The camera video quality diagnosis method is applied to a security system to form the security system supporting camera video quality diagnosis, and the limitations and the defects of the existing security system can be effectively overcome.
As shown in fig. 3, an embodiment of the present invention provides a video management platform system (or called management platform, video management system) including: the system comprises a business processing module 10, a storage module 20, a camera management module 30, an access module 40, a streaming media capability module 50, a quality diagnosis module 60, a camera 70 and an unmanned aerial vehicle camera 80. The camera 70 and the unmanned aerial vehicle camera 80 are cameras meeting the access requirement of the video management platform.
The service processing module 10 is configured to process service logic, such as video recording, snapshot and other logic, and has service capabilities of video recording, snapshot and the like.
The camera management module 30 is configured to manage camera information, which includes information such as longitude and latitude, installation height, and camera pointing direction.
The access module 40 is used for receiving and sending messages of the video management system, the camera and the unmanned aerial vehicle (or unmanned aerial vehicle camera), wherein the messages are control messages of the camera and the unmanned aerial vehicle (or unmanned aerial vehicle camera), specifically, the messages are used for connecting control signaling of the camera, forwarding the control signaling sent to the camera by the service processing module, and receiving the control signaling reported by the camera and sending the control signaling to the service processing module.
The streaming media capability module 50 is configured to interface the camera media stream and forward the media stream to a user that needs to use, and has service capabilities of video recording, snapshot and the like.
The quality diagnosis module 60 includes a quality diagnosis scheduling module for scheduling the unmanned aerial vehicle camera and the third-party video quality diagnosis system, and a video quality diagnosis module for analyzing the picture characteristics, specifically, a system for extracting the characteristics of the picture by using a computer vision algorithm, which generally needs to have GPU hardware and various vision algorithms.
The camera 70 is a device having video acquisition capability and network transmission capability, has a spherical video encoder, has a pan-tilt, and can rotate by 360 degrees.
Unmanned aerial vehicle camera 80 possesses wireless communication ability, GPS etc. and its airborne camera possesses video acquisition ability, possesses the cloud platform, can 360 degrees rotations for acquire the contrast picture of the camera position that awaits measuring.
The method for diagnosing the video quality of the camera by using the video management platform system (or called management platform, video management system) shown in fig. 3 includes the following steps: initiating a camera video quality diagnosis by the management platform; the management platform acquires information of a camera to be detected, wherein the information comprises longitude and latitude, camera orientation, installation height and other information, and assigns an unmanned aerial vehicle camera to move to the camera to be detected and arrange in parallel with the camera to be detected; the management platform takes the current picture of the camera to be detected as a standard, and moves the tripod head of the unmanned aerial vehicle camera to the same direction of the camera to be detected, so that the picture taken by the unmanned aerial vehicle camera is consistent with the camera to be detected; the management platform simultaneously triggers the video of the camera to be detected and the video of the unmanned aerial vehicle camera, and obtains pictures of the camera to be detected and the unmanned aerial vehicle camera at the same time point from the videos, wherein the pictures of the unmanned aerial vehicle camera are standard pictures, and the camera to be detected is a picture to be diagnosed; and the management platform performs characteristic analysis according to a certain algorithm by using the standard picture to obtain a standard characteristic value. The management platform performs characteristic analysis on the picture to be detected according to the same algorithm to obtain a characteristic value to be detected; and comparing the characteristic value to be detected with the standard characteristic value by the management platform to obtain a characteristic difference value, wherein if the characteristic difference values are similar, the camera to be detected is normal.
According to the embodiment, the diagnosis accuracy can be improved under the condition that the accuracy of the current algorithm is not optimized, meanwhile, the common camera does not need to be additionally transformed, the implementation cost is low, and the management and maintenance capability of the video management platform on the camera can be improved.
Fig. 4 is a specific flowchart of the system shown in fig. 3 for implementing the diagnosis process, and as shown in fig. 4, the method includes the following steps:
and S100, starting video diagnosis on a certain camera, wherein the camera is the camera to be detected.
In step S101, the quality diagnosis module 60 requests the camera management module 30 for the information of the camera to be tested.
Step S102, the quality diagnosis module 60 obtains the information of the camera to be detected and extracts the required key information, such as longitude and latitude, installation height, camera orientation and the like.
Step S103, the quality diagnosis module 60 sends an unmanned aerial vehicle movement instruction to the access module 40, wherein the instruction comprises information of the camera to be detected, such as longitude and latitude, installation height, camera orientation and the like.
Step S104, the access module 40 forwards the unmanned aerial vehicle moving instruction to the unmanned aerial vehicle camera 80.
And S105, the unmanned aerial vehicle camera 80 receives the unmanned aerial vehicle moving instruction, moves to the destination, hovers after reaching the destination, and moves the camera pan-tilt to be consistent with the orientation of the camera to be detected.
And S106, returning an arrival message after the unmanned aerial vehicle camera 80 arrives at the destination.
In step S107, the access module 40 forwards the arrival message to the quality diagnosis module 60.
And S108, the quality diagnosis module 60 sends a snapshot instruction to the camera to be tested and the unmanned aerial vehicle camera at the same time after the unmanned aerial vehicle reaches the designated position.
And step S109, the access module 40 forwards a snapshot instruction to the unmanned aerial vehicle camera 80.
Step S110, the access module 40 forwards the snapshot instruction to the camera 70.
And S111, moving the tripod head by the unmanned aerial vehicle camera 80 within a certain range of the current tripod head position, and simultaneously shooting pictures and recording the current tripod head absolute position.
And S112, uploading a plurality of pictures to the access module 40 by the unmanned aerial vehicle camera 80, and simultaneously attaching the absolute position information of the holder corresponding to the pictures.
In step S113, the camera 70 uploads the picture to the access module 40.
In step S114, the access module 40 returns the received pictures taken by the camera to be tested and the pictures taken by the plurality of drone cameras 80 to the quality diagnosis module 60 at the same time.
And S115, the quality diagnosis module 60 performs characteristic analysis and comparison on the received pictures, and takes the absolute position of the cradle head corresponding to the picture of the unmanned aerial vehicle camera with the highest similarity as the optimal position.
In step S116, the quality diagnosis module 60 sends a pan-tilt movement command of the drone camera to the access module 40, which causes the pan-tilt of the drone camera 80 to move to the optimal position.
And step S117, the access module 40 forwards the holder moving instruction.
And S118, the unmanned aerial vehicle camera 80 moves the holder to the optimal position.
And S119, the unmanned aerial vehicle camera 80 returns a response that the tripod head reaches the specified position.
In step S120, the access module 40 forwards the cradle head reaching command (i.e. the response of the cradle head reaching the specified position) to the quality diagnosis module 60.
In step S121, the quality diagnosis module 60 sends a video recording instruction to the camera 70 and the unmanned aerial vehicle camera 80 simultaneously.
In step S122, the access module 40 forwards the recording command to the camera 70.
Step S123, the access module 40 forwards the video recording instruction to the unmanned aerial vehicle camera 80.
And step S124, finishing the video recording of the unmanned aerial vehicle camera 80.
In step S125, the video recording by the camera 70 is completed.
In step S126, the access module 40 forwards the recording completion message to the quality diagnosis module 60.
In step S127, the quality diagnosis module 60 obtains the pictures at the same time point in the video file of the camera to be tested and the video file of the camera of the unmanned aerial vehicle, so as to obtain the picture of the camera to be tested and the picture of the camera of the unmanned aerial vehicle (i.e. the standard picture).
In step S128, the quality diagnosis module 60 performs characteristic analysis on the picture to be detected to obtain a characteristic value to be detected.
And S129, the quality diagnosis module 60 performs characteristic analysis on the standard picture to obtain a standard characteristic value.
Step S130, the quality diagnosis module 60 compares the obtained feature value to be detected and the standard feature value, and determines whether the quality of the picture of the camera to be detected is normal according to the difference value of the two feature values.
And step S131, finishing video diagnosis.
According to the method, the accuracy of the current algorithm does not need to be optimized, the common camera does not need to be additionally transformed, the diagnosis accuracy is high, the implementation cost is low, and the management and maintenance capability of the video management platform on the camera can be improved.
As shown in fig. 5, an embodiment of the present invention provides a camera video quality diagnosis apparatus 20, where the apparatus 20 includes a memory 21, a processor 22, a program stored in the memory and operable on the processor, and a data bus 23 for implementing connection communication between the processor 21 and the memory 22, and the program, when executed by the processor, implements the steps of the camera video quality diagnosis method described above.
Embodiments of the present invention also provide a storage medium storing one or more programs, which are executable by one or more processors to implement the foregoing steps of the camera video quality diagnosis method.
One of ordinary skill in the art will appreciate that all or some of the steps of the methods, systems, functional modules/units in the devices disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
In a hardware implementation, the division between functional modules/units mentioned in the above description does not necessarily correspond to the division of physical components; for example, one physical component may have multiple functions, or one function or step may be performed by several physical components in cooperation. Some or all of the physical components may be implemented as software executed by a processor, such as a central processing unit, digital signal processor, or microprocessor, or as hardware, or as an integrated circuit, such as an application specific integrated circuit. Such software may be distributed on computer readable media, which may include computer storage media (or non-transitory media) and communication media (or transitory media). The term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data, as is well known to those of ordinary skill in the art. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, Digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can accessed by a computer. In addition, communication media typically embodies computer readable instructions, data structures, program modules or other data in a modulated data signal such as a carrier wave or other transport mechanism and includes any information delivery media as known to those skilled in the art.
The preferred embodiments of the present invention have been described above with reference to the accompanying drawings, and are not to be construed as limiting the scope of the invention. Any modifications, equivalents and improvements which may occur to those skilled in the art without departing from the scope and spirit of the present invention are intended to be within the scope of the claims.
Claims (10)
1. A camera video quality diagnostic method, comprising the steps of:
acquiring a first video file recorded by a camera to be tested and a second video file recorded by an unmanned aerial vehicle camera, wherein the first video file and the second video file are respectively obtained by recording the camera to be tested and the unmanned aerial vehicle camera under the same shooting condition;
respectively acquiring a first image and a second image within a preset time range from the first video file and the second video file;
and determining a video quality diagnosis result of the camera to be detected by performing feature analysis on the first image and the second image.
2. The method of claim 1, wherein obtaining a first video file recorded by a camera to be tested and a second video file recorded by an unmanned aerial vehicle camera comprises:
sending a video recording instruction to the camera to be detected and the unmanned aerial vehicle camera so that the camera to be detected and the unmanned aerial vehicle camera record the first video file and the second video file respectively according to the video recording instruction;
and receiving a first video file sent by the camera to be tested and a second video file sent by the unmanned aerial vehicle camera.
3. The method of claim 1, wherein determining the video quality diagnostic result of the camera under test by performing feature analysis on the first image and the second image comprises:
respectively carrying out feature analysis on the first image and the second image by utilizing a first video quality diagnosis algorithm to obtain a first image feature value and a second image feature value;
and determining a video quality diagnosis result of the camera to be detected according to the difference value of the first image characteristic value and the second image characteristic value.
4. The method of claim 3, wherein determining the video quality diagnosis result of the camera under test according to the difference between the first image feature value and the second image feature value comprises:
and if the difference value of the first image characteristic value and the second image characteristic value is smaller than a preset range, determining that the video quality of the camera to be detected meets the requirement.
5. The method according to any one of claims 1-4, wherein prior to obtaining the first video file recorded by the camera under test and the second video file recorded by the drone camera, the method further comprises:
acquiring the position information and the camera orientation information of the camera to be detected;
and sending a moving instruction carrying the position information and the orientation information of the camera to the unmanned aerial vehicle camera, so that the unmanned aerial vehicle camera moves to the position near the position of the camera to be detected according to the position information in the moving instruction and hovers, and adjusting the orientation of a camera pan-tilt to be consistent with the orientation of the camera to be detected according to the orientation information of the camera in the moving instruction.
6. The method of claim 5, wherein after sending the movement instruction to the drone camera carrying the position information and camera orientation information, the method further comprises:
receiving an arrival message sent by the unmanned aerial vehicle camera after the unmanned aerial vehicle camera moves to the position near the position of the camera to be detected and the orientation of the arrival message is consistent with that of the camera to be detected;
and after receiving the arrival message, adjusting the camera pan-tilt of the unmanned aerial vehicle camera to a specified position.
7. The method of claim 6, wherein adjusting the camera pan head of the drone camera to a specified position comprises:
sending a snapshot instruction to the camera to be tested and the unmanned aerial vehicle camera;
receiving a first snapshot image shot by the camera to be detected according to the snapshot instruction, a plurality of second snapshot images shot by the unmanned aerial vehicle camera according to the snapshot instruction and a plurality of corresponding cradle head absolute positions, wherein each second snapshot image is obtained by shooting a camera cradle head of the unmanned aerial vehicle camera at different cradle head absolute positions;
respectively determining the similarity of the first snapshot image and each second snapshot image, and acquiring the absolute position of the cradle head corresponding to any second snapshot image with the similarity within the similarity threshold range as an appointed position;
and sending a moving instruction carrying the appointed position of the camera holder to the unmanned aerial vehicle camera, so that the camera holder of the unmanned aerial vehicle camera moves to the appointed position, and the camera holder records the second video file at the optimal position.
8. The method of claim 7, wherein the separately determining the similarity of the first snap-shot image to each of the second snap-shots comprises:
and respectively comparing the characteristics of the first snapshot image with each second snapshot image to determine the similarity of the first snapshot image and each second snapshot image.
9. A camera video quality diagnosis apparatus characterized by comprising a memory, a processor, a program stored on the memory and executable on the processor, and a data bus for realizing connection communication between the processor and the memory, the program realizing the steps of the camera video quality diagnosis method according to any one of claims 1 to 8 when executed by the processor.
10. A storage medium for computer-readable storage, characterized in that the storage medium stores one or more programs executable by one or more processors to implement the steps of the camera video quality diagnosis method according to any one of claims 1 to 8.
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