CN115474038A - Camera shielding detection method and device, electronic equipment and storage medium - Google Patents
Camera shielding detection method and device, electronic equipment and storage medium Download PDFInfo
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- CN115474038A CN115474038A CN202211109792.7A CN202211109792A CN115474038A CN 115474038 A CN115474038 A CN 115474038A CN 202211109792 A CN202211109792 A CN 202211109792A CN 115474038 A CN115474038 A CN 115474038A
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- H04N17/002—Diagnosis, testing or measuring for television systems or their details for television cameras
Abstract
The application discloses a camera shielding detection method and device, electronic equipment and a storage medium. Firstly, setting a time interval and a threshold value for carrying out camera shielding detection comparison; calculating the similarity index of the current frame video image and the previous frame video image; when the similarity index is lower than the single comparison limit value, the current frame image and the current time are traced back to the similarity index of each frame image cached in a preset comparison time interval for statistical comparison, the proportion of the number of abnormal frames is counted, if the proportion exceeds a preset threshold value, the camera is judged to be shielded in the time interval, and secondary statistical alarm is triggered. According to the method and the device, single similarity comparison and comparison of abnormal frame number proportion in a time interval are respectively carried out, and the accuracy of camera shielding detection is improved.
Description
Technical Field
The present invention relates to the field of video image processing technologies, and in particular, to a method and an apparatus for detecting camera occlusion, an electronic device, and a storage medium.
Background
Monitoring cameras are installed in many scenes at home and abroad, and the system is used in the fields of public safety, traffic and the like. Some surveillance cameras are easily sheltered from by lawless persons with modes such as foreign matter shelter from, or cause the camera to shelter from by the foreign matter because of weather reason. The existing scheme is to adopt manual maintenance camera to shelter from.
However, the workload of manually monitoring and maintaining a large number of cameras in real time is very large, and the cameras are greatly influenced by factors such as environment and the like, so that misjudgment is easily caused.
Disclosure of Invention
Based on this, the embodiment of the application provides a camera occlusion detection method and device, an electronic device and a storage medium, through an image processing technology, whether a camera is occluded or not is automatically detected, an alarm response can be made within millisecond-level time, the maintenance workload of the camera is greatly reduced, and the alarm response time is greatly shortened.
In a first aspect, a method for detecting camera occlusion is provided, where the method includes:
setting a time interval for carrying out camera shielding detection comparison, a single comparison limit value, a statistical alarm threshold value and a proportion threshold value;
calculating the similarity index S between the current frame video image and the previous frame video image 1 ；
When the similarity index S 1 When the comparison time is lower than the single comparison limit value, calculating a similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S is 1 And the similarity index S 2 When the difference value is higher than the statistic alarm threshold value, marking the video image of any frame as a statistic abnormal frame; traversing all frames in the time interval to obtain all statistical abnormal frames in the time interval, and calculating the number of the statistical abnormal frames;
and when the proportion of the number of the statistical abnormal frames in all the frames in the time interval is higher than the proportion threshold value, judging that the camera is shielded in the time interval.
Optionally, the method further comprises:
and updating or adding dynamic cache data, wherein the cache data is image data of each frame of image.
Optionally, a similarity index S between the current frame video image and the previous frame video image is calculated 1 The method comprises the following steps:
according to the formula
simP(x,y)＝abs(f(x,y)-fp(x,y))
Determining a pixel similarity characteristic value; wherein, x and y represent the coordinate of any pixel in the image, f (x and y) is the YUV value of the current frame video image, fp (x and y) is the YUV value of the previous frame video image, simP (x and y) is the pixel similarity characteristic value of the current frame video image at the position of the pixel point (x and y);
and calculating the average value based on all pixel similarity characteristic values in the video image to obtain a similarity index S 1 。
Optionally, when the similarity index S 1 And when the comparison result is higher than the single comparison limit value, judging that the camera is shielded at the current time.
Optionally, when the proportion of the counted number of abnormal frames in all frames in the time interval is higher than the proportion threshold, it is determined that the camera is blocked in the time interval, including:
according to
And determining the proportion P occupied by the number of the statistical abnormal frames in all frames in the time interval, wherein F (out) represents the number of the statistical abnormal frames in the time interval, and F (all) represents the number of all frames in the time interval.
In a second aspect, a camera occlusion detection device is provided, the device comprising:
the setting module is used for setting a time interval for carrying out camera shielding detection comparison, a single comparison limit value, a statistical alarm threshold value and a proportion threshold value;
a first calculating module for calculating similarity index S between the current frame video image and the previous frame video image 1 ；
A second calculation module for calculating the similarity index S 1 When the comparison limit value is lower than the single comparison limit value, calculating the similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S is 1 And the similarity index S 2 When the difference value of the random frame video image is higher than the statistical alarm threshold value, marking the random frame video image as a statistical abnormal frame; traversing all frames in the time interval to obtain all statistical abnormal frames in the time interval, and calculating the number of the statistical abnormal frames;
and the judging module is used for judging that the camera is shielded in the time interval when the proportion of the counted abnormal frame number in all frames in the time interval is higher than the proportion threshold value.
Optionally, the apparatus further comprises:
and the cache module is used for updating or adding dynamic cache data, wherein the cache data is image data of each frame of image.
In a third aspect, an electronic device is provided, which includes a memory and a processor, where the memory stores a computer program, and the processor implements the camera occlusion detection method according to any one of the above first aspects when executing the computer program.
In a fourth aspect, a computer-readable storage medium is provided, on which a computer program is stored, and the computer program, when executed by a processor, implements the camera occlusion detection method according to any one of the first aspect.
In the technical scheme provided by the embodiment of the application, firstly, a time interval for carrying out camera shielding detection comparison, a single comparison limit value, a statistical alarm threshold value and a proportion threshold value are set; calculating the similarity index S between the current frame video image and the previous frame video image 1 (ii) a When the similarity index S 1 When the similarity is lower than the single comparison limit value, calculating the similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S 1 And similarity index S 2 When the difference value is higher than the statistical alarm threshold value, marking the video image of any frame as a statistical abnormal frame; traversing all frames in the time interval to obtain all statistical abnormal frames in the time interval, and calculating the number of the statistical abnormal frames; and when the proportion of the number of the statistical abnormal frames in all the frames in the time interval is higher than a proportion threshold value, judging that the camera is shielded in the time interval.
The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:
(1) The comparison of the number proportion of the abnormal frames in a single comparison and a time interval is respectively carried out, so that the accuracy of the camera shielding detection is improved.
(2) The number of dynamic images during the current shielding detection comparison is 2, the time detection automatic alarm response time reaches millisecond level, and the detection response speed is high.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It should be apparent that the drawings in the following description are merely exemplary, and that other embodiments can be derived from the drawings provided by those of ordinary skill in the art without inventive effort.
Fig. 1 is a flowchart illustrating steps of a method for detecting camera occlusion according to an embodiment of the present disclosure;
FIG. 2 is a flowchart illustrating steps of a method for detecting camera occlusion according to an alternative embodiment of the present disclosure;
fig. 3 is a block diagram of a camera occlusion detection apparatus according to an embodiment of the present application;
fig. 4 is a schematic view of an electronic device according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application.
In the description of the present invention, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements specifically listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus or additional steps or elements based on further optimization of the inventive concept.
In order to solve the problem of real-time detection of camera shielding during manual maintenance, the invention automatically detects whether the camera is shielded or not through an image processing technology, can make an alarm response within millisecond-level time, greatly reduces the maintenance workload of the camera and greatly shortens the alarm response time. Referring to fig. 1, a flowchart of a method for detecting camera occlusion provided in an embodiment of the present application is shown, where the method may include the following steps:
In the embodiment of the present application, the setting of the comparison threshold specifically includes:
step 1011, setting a time interval T for comparison, wherein the shorter the time interval is, the shorter the alarm response time of statistical comparison is, the shorter the time interval is, and the time interval can be flexibly set according to an application scene, and the value is generally 1000ms.
Step 1012, counting the proportion P threshold of the abnormal frames in the total number of frames within the comparison time interval, with the default value of 0.6.
Step 1013, setting a threshold value of the comparison difference value of the similarity index of the camera video image in the continuous statistical comparison process, that is, when performing statistical comparison, marking the current image as a statistical abnormal frame when the similarity of the current image and the comparison image is different to a certain extent. The default value is set to 0.75.
And 1014, setting an abnormal threshold value of the similarity index value of the single comparison of the video images of the camera, namely, performing alarm response when the similarity index of any two frames of video images is lower than a certain value. The default values are set as: 0.5.
102, calculating similarity index S between the current frame video image and the previous frame video image 1 。
In the embodiment of the application, the current frame video image and the previous frame video are calculatedSimilarity index S of frequency image 1 Including according to the formula
simP(x,y)＝abs(f(x,y)-fp(x,y))
Determining a pixel similarity characteristic value; wherein, x and y represent the coordinate of any pixel in the image, f (x and y) is the YUV value of the current frame video image, fp (x and y) is the YUV value of the previous frame video image, simP (x and y) is the pixel similarity characteristic value of the current frame video image at the position of the pixel point (x and y);
and carrying out average calculation on the similarity characteristic graphs of the current video image and the previous frame image to obtain the similarity index of the images.
In the embodiment of the present application, when the similarity index S 1 And when the comparison result is higher than the single comparison limit value, judging that the camera is shielded at the current time. That is, if the value of S exceeds the limit value of the single comparison in step 1014, the camera is considered to be shielded at this time, and an alarm is triggered immediately.
In this embodiment of the application, if the similarity index of the current video image obtained in step 102 is within the threshold range, then the time range T set in step 1011 is cached backwards at the current time, and comparison between the N frames of video images in this time period and the similarity index of the current video image is performed in sequence, that is, it is detected whether the difference between the similarity indexes of the N-th (N =1 … N) frame of video image and the current frame of video image exceeds the set statistical alarm threshold in step 1013.
And if the statistical alarm threshold value of the similarity index is exceeded, marking the nth frame of video image as a statistical abnormal frame, and if the statistical alarm threshold value of the similarity index is not exceeded, marking the nth frame of video image as a normal frame.
And calculating the proportion P of the statistical abnormal frames in the N frames of video images in the time period to the total number of frames, and when the P is about a certain threshold value (the set value in the step 1012), determining that the camera in the time period is shielded, and performing secondary statistical alarm response.
According to
And determining the proportion P occupied by the number of the statistical abnormal frames in all the frames in the time interval, wherein F (out) represents the number of the statistical abnormal frames in the time interval, and F (all) represents the number of all the frames in the time interval, and the statistical abnormal frames refer to the frames with similarity indexes reaching the participation of statistical abnormality.
The secondary statistical alarm refers to secondary alarm performed after the proportion of the statistical abnormal frame exceeds a proportion threshold value through statistics.
And step 104, when the proportion of the counted abnormal frame number in all frames in the time interval is higher than a proportion threshold value, judging that the camera is shielded in the time interval.
If the similarity index of the current video image is within the threshold range, the current frame image and the current time are traced back to each frame image cached in a preset comparison time interval for comparison of the similarity index, then the proportion of all the statistical abnormal frames in the total frame number is counted according to the comparison result, and after the proportion is larger than a certain threshold value, the lens is detected to be shielded and an alarm is sent out.
After step 104, further comprising:
and step 105, updating or adding dynamic cache data, wherein the cache data is image data of each frame of image.
In the embodiment of the application, dynamic cache data is updated or added, and the cache data is image data of each frame of image. The length of the buffer queue is the number of video frames of the T time length.
As shown in fig. 2, a processing flow for detecting camera occlusion by the method of the present application is provided, which specifically includes:
(1) Setting a detection comparison time threshold;
(2) Setting a threshold value for detecting the image similarity index difference value;
(3) Setting an alarm threshold value for detecting the image similarity index;
(4) Calculating the similarity index of the current image and the previous frame image;
(5) Carrying out statistical comparison of similarity indexes between the current image and the image in the caching period;
(6) Updating or adding the cached data.
In conclusion, the method and the device extract the current frame image, calculate the similarity index between the current frame image and the previous frame image, compare the calculated similarity index with the preset similarity threshold value, and determine that the camera is shielded and send an alarm when the similarity index is lower than the preset threshold value; and the current frame image and the current time are traced back to the similarity index of each frame image cached in a preset comparison time interval for statistical comparison, the proportion of the abnormal frame is counted, and a secondary statistical alarm is triggered when the proportion exceeds a preset threshold value.
Please refer to fig. 3, which illustrates a block diagram of a camera occlusion detection apparatus 300 according to an embodiment of the present application. As shown in fig. 3, the apparatus 300 may include: a setting module 301, a first calculating module 302, a second calculating module 303 and a determining module 304.
The setting module 301 is configured to set a time interval for performing camera occlusion detection comparison, a single comparison limit value, a statistical alarm threshold value, and a proportional threshold value;
a first calculating module 302, configured to calculate a similarity index S between a current frame video image and a previous frame video image 1 ；
A second calculating module 303, for calculating a similarity index S 1 When the similarity is lower than the single comparison limit value, calculating the similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S 1 And similarity index S 2 When the difference value is higher than the statistical alarm threshold value, marking the video image of any frame as a statistical abnormal frame; traversal timeAll frames in the interval are obtained, and all statistical abnormal frames in the time interval are obtained;
the determining module 304 is configured to determine that the camera is blocked in the time interval when the proportion of the counted abnormal frame number in all frames in the time interval is higher than a proportion threshold.
In an alternative embodiment of the present application, the apparatus 300 further includes a buffer module 305, configured to update or add dynamic buffer data, where the buffer data is image data of each frame of image.
For specific limitations of the camera occlusion detection device, reference may be made to the above limitations on the camera occlusion detection method, which are not described herein again. All modules in the camera occlusion detection device can be completely or partially realized by software, hardware and a combination thereof. The modules can be embedded in a hardware form or independent from a processor in the computer device, and can also be stored in a memory in the computer device in a software form, so that the processor can call and execute operations corresponding to the modules.
In one embodiment, an electronic device is provided, which may be a computer, and the internal structure thereof may be as shown in fig. 4. The electronic device includes a processor, a memory, and a network interface connected by a system bus. Wherein the processor of the device is configured to provide computing and control capabilities. The memory of the device comprises a nonvolatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program, and a database. The internal memory provides an environment for the operation of an operating system and computer programs in the non-volatile storage medium. The database of the computer device is used for camera occlusion detection data. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement a camera occlusion detection method.
Those skilled in the art will appreciate that the architecture shown in fig. 4 is merely a block diagram of some of the structures associated with the disclosed aspects and is not intended to limit the computing devices to which the disclosed aspects apply, as particular computing devices may include more or less components than those shown, or may combine certain components, or have a different arrangement of components.
In an embodiment of the present application, a computer-readable storage medium is provided, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the above-mentioned camera occlusion detection method.
The implementation principle and technical effect of the computer-readable storage medium provided by this embodiment are similar to those of the above-described method embodiment, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above may be implemented by hardware instructions of a computer program, which may be stored in a non-volatile computer-readable storage medium, and when executed, may include the processes of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in the embodiments provided herein may include non-volatile and/or volatile memory, among others. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in M forms, such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SyMchlimk) DRAM (SLDRAM), raMbus (RaMbus) direct RAM (RDRAM), direct RaMbus Dynamic RAM (DRDRAM), and RaMbus Dynamic RAM (RDRAM), among others.
All the technical features of the above embodiments can be arbitrarily combined (as long as there is no contradiction between the combinations of the technical features), and for brevity of description, all the possible combinations of the technical features in the above embodiments are not described; these examples, which are not explicitly described, should be considered to be within the scope of the present description.
The present application has been described in considerable detail with reference to certain embodiments and examples thereof. It should be understood that several conventional adaptations or further innovations of these specific embodiments may also be made based on the technical idea of the present application; however, such conventional modifications and further innovations may also fall within the scope of the claims of the present application as long as they do not depart from the technical idea of the present application.
Claims (9)
1. A camera occlusion detection method, the method comprising:
setting a time interval for carrying out camera shielding detection comparison, a single comparison limit value, a statistical alarm threshold value and a proportion threshold value;
calculating the similarity index S between the current frame video image and the previous frame video image 1 ；
When the similarity index S 1 When the comparison time is lower than the single comparison limit value, calculating a similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S is 1 And the similarity index S 2 When the difference value is higher than the statistic alarm threshold value, marking the video image of any frame as a statistic abnormal frame;
traversing all frames in the time interval to obtain all statistical abnormal frames in the time interval, and calculating the number of the statistical abnormal frames;
and when the proportion of the counted abnormal frame number in all frames in the time interval is higher than the proportion threshold value, judging that the camera is shielded in the time interval.
2. The method of claim 1, further comprising:
and updating or adding dynamic cache data, wherein the cache data is image data of each frame of image.
3. The method of claim 1, wherein the similarity index S between the current frame video image and the previous frame video image is calculated 1 The method comprises the following steps:
according to the formula
simP(x,y)＝abs(f(x,y)-fp(x,y))
Determining a pixel similarity characteristic value; wherein, x and y represent the coordinate of any pixel in the image, f (x and y) is the YUV value of the current frame video image, fp (x and y) is the YUV value of the previous frame video image, simP (x and y) is the pixel similarity characteristic value of the current frame video image at the position of the pixel point (x and y);
and calculating the average value based on all pixel similarity characteristic values in the video image to obtain a similarity index S 1 。
4. The method of claim 1, wherein the similarity index S is a measure of similarity between the two images 1 And when the comparison result is higher than the single comparison limit value, judging that the camera is shielded at the current time.
5. The method according to claim 1, wherein when the proportion of the statistical abnormal frame number in all frames in a time interval is higher than the proportion threshold, determining that the camera is blocked in the time interval comprises:
according to
And determining the proportion P occupied by the number of the statistical abnormal frames in all frames in the time interval, wherein F (out) represents the number of the statistical abnormal frames in the time interval, and F (all) represents the number of all frames in the time interval.
6. A camera occlusion detection device, the device comprising:
the setting module is used for setting a time interval for carrying out camera shielding detection comparison, a single comparison limit value, a statistical alarm threshold value and a proportion threshold value;
a first calculating module for calculating the current frame video image and the previous oneSimilarity index S of frame video image 1 ；
A second calculation module for calculating the similarity index S 1 When the comparison time is lower than the single comparison limit value, calculating a similarity index S of the current frame video image and any frame video image in the time interval 2 When the similarity index S is 1 And the similarity index S 2 When the difference value of the random frame video image is higher than the statistical alarm threshold value, marking the random frame video image as a statistical abnormal frame; traversing all frames in the time interval to obtain all statistical abnormal frames in the time interval, and calculating the number of the statistical abnormal frames;
and the judging module is used for judging that the camera is shielded in the time interval when the proportion of the counted abnormal frame number in all frames in the time interval is higher than the proportion threshold value.
7. The apparatus of claim 6, further comprising:
and the cache module is used for updating or adding dynamic cache data, wherein the cache data is image data of each frame of image.
8. An electronic device, comprising a memory and a processor, the memory storing a computer program which, when executed by the processor, implements the camera occlusion detection method of any of claims 1 to 5.
9. A computer-readable storage medium, having stored thereon a computer program which, when executed by a processor, implements the camera occlusion detection method of any of claims 1 to 5.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115861624A (en) * | 2023-03-03 | 2023-03-28 | 天津所托瑞安汽车科技有限公司 | Method, device and equipment for detecting shielding of camera and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN115861624A (en) * | 2023-03-03 | 2023-03-28 | 天津所托瑞安汽车科技有限公司 | Method, device and equipment for detecting shielding of camera and storage medium |
| CN115861624B (en) * | 2023-03-03 | 2023-05-30 | 天津所托瑞安汽车科技有限公司 | Method, device, equipment and storage medium for detecting occlusion of camera |
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