Intelligent grouping method and system for monitoring videos, terminal and storage medium

Abstract

The invention discloses an intelligent grouping method and system for monitoring videos, a terminal and a storage medium, and the method comprises the steps: obtaining the names and group names of all monitors needing to be processed, obtaining a monitoring content screenshot corresponding to each monitor, selecting a part of images from all the monitoring content screenshots, carrying out the manual marking, and marking a scene label; obtaining keywords of content tags, groups and names of all the images by using a third-party tool, and training the monitoring video classification model in combination with the scene tags to obtain a trained monitoring video classification model; and obtaining to-be-classified monitoring videos needing to be classified, inputting the to-be-classified monitoring videos into the trained monitoring video classification model, and outputting a classification result of the to-be-classified monitoring videos. According to the method, keywords of image names and image content labels are obtained, the relation between vocabularies and scenes is searched, a classification model is established through the relation, and after monitoring is newly added, automatic classification is carried out after classification model processing.

Description

Technical Field

[0001] The present invention relates to the technical field of video surveillance classification, and in particular to an intelligent grouping method, system, terminal and computer-readable storage medium for surveillance videos. Background Art

[0002] With the advancement of technology, there are more and more types of communication equipment. In the field of emergency command, there are many types of media acquisition equipment, including drones, portable phones, fixed phones, surveillance, etc. These media acquisition devices may be used in different regions and scenarios. The traditional management method is that the superior manages all the monitoring of the subordinate. This grouping method is not suitable for all scenarios, such as querying the monitoring of all intersections of a company, querying the monitoring of all halls of a company, etc.

[0003] Therefore, the prior art still needs to be improved and developed. Summary of the invention

[0004] The main purpose of the present invention is to provide a method, system, terminal and computer-readable storage medium for intelligent grouping of surveillance videos, aiming to solve the problem in the prior art that when managing surveillance videos, the superior manages all the surveillance of the subordinates, and this grouping method is not suitable for all scenarios, resulting in unreasonable classification of surveillance videos.

[0005] To achieve the above object, the present invention provides a method for intelligently grouping surveillance videos, the method comprising the following steps:

[0006] Get the names and group names of all monitoring items that need to be processed, and get the monitoring content screenshots corresponding to each monitoring item. Select some images from all monitoring content screenshots for manual annotation and add scene labels.

[0007] Use a third-party tool to obtain the content labels, groupings, and name keywords of all images, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model;

[0008] Obtain a surveillance video to be classified, input the surveillance video to be classified into a trained surveillance video classification model, and output a classification result of the surveillance video to be classified.

[0009] Optionally, the intelligent grouping method of surveillance videos, wherein the obtaining of the names and group names of all surveillances to be processed, obtaining a surveillance content screenshot corresponding to each surveillance, selecting some images from all surveillance content screenshots for manual annotation, and adding scene labels, specifically includes:

[0010] Get the names and group names of all monitorings that need to be processed, and get the monitoring content screenshots corresponding to each monitoring, where the monitoring content screenshots are named after the corresponding monitoring names;

[0011] Select some images from all surveillance content screenshots for manual annotation and add scene labels. The manually annotated images are used as the standard results of model classification.

[0012] Optionally, in the intelligent grouping method of surveillance videos, the process of obtaining a screenshot of the surveillance content corresponding to each surveillance includes: receiving the stream, parsing I frames and saving as pictures.

[0013] Optionally, the intelligent grouping method of surveillance videos, wherein the use of a third-party tool to obtain the content tags, grouping and name keywords of all images, and training the surveillance video classification model in combination with the scene tags to obtain the trained surveillance video classification model, specifically includes:

[0014] Use third-party tools to obtain keywords for content tags, groups, and names of all selected images;

[0015] Organize the scene labels that need to be used, write the correspondence between scene labels and keywords and image content labels, train the surveillance video classification model until it meets the requirements, and obtain a trained surveillance video classification model.

[0016] Optionally, in the intelligent grouping method for surveillance videos, the step of training the surveillance video classification model until the requirements are met specifically includes:

[0017] After each surveillance video classification model training is completed, it is compared with the standard set obtained by manual grouping;

[0018] If the difference with the standard set is greater than the preset requirement, the correspondence between keywords and tags and scene tags is modified until the classification is reasonable;

[0019] For manually marked unreasonable words, select Delete and add reasonable words to the vocabulary library.

[0020] Optionally, in the intelligent grouping method for surveillance videos, the vocabulary library includes all keywords and tags.

[0021] Optionally, the intelligent grouping method of surveillance videos, wherein the obtaining of surveillance videos to be classified, inputting the surveillance videos to be classified into a trained surveillance video classification model, and outputting classification results of the surveillance videos to be classified, specifically includes:

[0022] Obtaining a surveillance video to be classified, and inputting the surveillance video to be classified into a surveillance video classification model whose model accuracy meets the requirements;

[0023] The surveillance video to be classified is marked with a scene label through a surveillance video classification model, and a classification result of the surveillance video to be classified is output, and the classification result is obtained by grouping according to the relationship between the scene label and the image.

[0024] In addition, to achieve the above-mentioned purpose, the present invention also provides an intelligent grouping system for monitoring videos, wherein the intelligent grouping system for monitoring videos comprises:

[0025] The data processing module is used to obtain the names and group names of all monitoring items that need to be processed, and obtain the monitoring content screenshots corresponding to each monitoring item. Some images are selected from all monitoring content screenshots for manual annotation and are marked with scene labels.

[0026] The model training module is used to obtain the content labels, grouping and name keywords of all images using third-party tools, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model;

[0027] The video classification module is used to obtain the surveillance video to be classified, input the surveillance video to be classified into the trained surveillance video classification model, and output the classification result of the surveillance video to be classified.

[0028] In addition, to achieve the above-mentioned purpose, the present invention also provides a terminal, wherein the terminal includes: a memory, a processor, and an intelligent grouping program for surveillance videos stored in the memory and runnable on the processor, and when the intelligent grouping program for surveillance videos is executed by the processor, the steps of the intelligent grouping method for surveillance videos as described above are implemented.

[0029] In addition, to achieve the above-mentioned purpose, the present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a smart grouping program for surveillance videos, and when the smart grouping program for surveillance videos is executed by a processor, the steps of the smart grouping method for surveillance videos as described above are implemented.

[0030] In the present invention, the names and group names of all monitorings that need to be processed are obtained, and the screenshots of the monitoring content corresponding to each monitoring are obtained. Some images are selected from all the monitoring content screenshots for manual annotation and are marked with scene labels; a third-party tool is used to obtain the content labels, grouping and name keywords of all images, and the monitoring video classification model is trained in combination with the scene labels to obtain a trained monitoring video classification model; the monitoring videos to be classified that need to be classified are obtained, and the monitoring videos to be classified are input into the trained monitoring video classification model, and the classification results of the monitoring videos to be classified are output. The present invention uses an intelligent way to group monitoring videos, obtains the keywords of the image names and the image content labels, and finds the relationship between these words and scenes, and finally establishes a classification model through the relationship. After the monitoring is added, it will be automatically classified after being processed by the classification model. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] Figure 1 It is a flow chart of a preferred embodiment of the intelligent grouping method of monitoring video of the present invention;

[0032] Figure 2 It is a flow chart of the data acquisition, model training and monitoring classification process in a preferred embodiment of the intelligent grouping method of monitoring video of the present invention;

[0033] Figure 3 It is a structural diagram of a preferred embodiment of the intelligent grouping system for monitoring video of the present invention;

[0034] Figure 4 It is a structural diagram of a preferred embodiment of the terminal of the present invention. DETAILED DESCRIPTION

[0035] In order to make the purpose, technical solution and advantages of the present invention clearer and more specific, the present invention is further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0036] The intelligent grouping method of monitoring video described in the preferred embodiment of the present invention is as follows: Figure 1 and Figure 2 As shown, the intelligent grouping method of the surveillance video includes the following steps:

[0037] Step S10: Obtain the names and group names of all monitoring items that need to be processed, and obtain the monitoring content screenshots corresponding to each monitoring item. Select some images from all monitoring content screenshots for manual annotation and add scene labels.

[0038] Specifically, obtain the names and group names of all monitoring that need to be processed (there are only all groups in advance, and no groups are set for monitoring. The group name is AI that can recognize image classification. Generally, monitoring is grouped by area. The advantage of this is that it provides another dimension of monitoring grouping for easy viewing), and obtain the monitoring content screenshots corresponding to each monitoring (take one or more screenshots, all monitoring in the system has a unique ID, screenshot name: Id_name_serial number.png, so that the group processing results are associated with specific monitoring. The core of obtaining screenshots is completed by ffmpeg, which mainly includes three steps of receiving streams, parsing I frames, and saving as pictures). The monitoring content screenshots are named after the corresponding monitoring; select some images from all monitoring content screenshots for manual annotation, and add scene labels, and use the manually annotated images as standard results of model classification.

[0039] Step S20: Use a third-party tool to obtain the content tags, grouping and name keywords of all images, and train the surveillance video classification model in combination with the scene tags to obtain a trained surveillance video classification model.

[0040] Specifically, a third-party tool is used to obtain the content labels, groups and keywords of the names of all selected images. Keywords are the result output of automatic recognition of images by AI. Untrained keywords may not be used as groups, and keywords with the highest scores after training can be used as groups. By decomposing phrases in the monitoring names, groups required by the emergency industry may also be obtained. For example, the names contain elementary school, school, etc. For example, keywords include: cabinet, indoor corner, display screen, carpet, brick and stone, bag, thick fog, astronomical photography, polished tile, aluminum-plastic plate, birds, elevator, modern architecture, machinery and equipment, bicycle, canyon, etc.; manual labels are added during model training. The second recognition result will include manual labels and some picture vocabulary. If the manual label is not ranked first, more image content will be annotated from the picture and retraining will be performed until the first or first few recognition results are the expected content labels; sort out the scene labels that need to be used, write the correspondence between scene labels and keywords and image content labels, train the monitoring video classification model until it meets the requirements, and obtain a trained monitoring video classification model.

[0041] Iterate according to the obtained keywords and image content labels until a suitable model is obtained; sort out commonly used scene labels, image content labels and keywords, and words with low probability of appearing cannot be used as training data sets. According to the keywords and image content labels of manually annotated images, manually write rules to train the model (for example: ponds belong to agriculture, forestry, animal husbandry and fishery, intersections belong to transportation, gas stations and tank areas belong to hazardous chemicals). Test the accuracy of the model until it meets the requirements. After each model training is completed, compare it with the standard set obtained by manual grouping (that is, the correspondence between the monitored corresponding images and scene labels); if there is a significant gap with the standard set, modify the correspondence between keywords and labels and scene labels until the classification is basically reasonable; for unreasonable manually annotated words, choose to delete them. Similarly, reasonable words are added to the vocabulary library (including all keywords and labels).

[0042] This is a model training process. Basic AI recognizes individual items, such as spoons, stoves, tables, signs, pipelines, oil guns, fire extinguishers, etc. However, it needs to be able to recognize kitchens, schools, gas stations, etc. Many images of different kitchens will be found, important distinguishing elements in the images will be marked, and the kitchens will be grouped. The third-party tools will be trained so that the groups recognized by the third-party tools contain the given labels. After multiple trainings, the scores of the defined labels will be higher and higher. After training, the third-party tools will recognize similar images and give the expected groups. The images for training are selected manually, and the identification and labels in the images are all manual, and one category of images is selected at a time. Manual scene labeling is a classification of concern in the emergency industry.

[0043] Choose appropriate development tools; use a normal 4-core 16GB memory with Windows operating system to develop the software, compile the software using VScode, and need to use third-party model training tools (such as Baidu Feipian), third-party libraries (such as ffmpeg) and image annotation tools.

[0044] Step S30: Obtain a surveillance video to be classified, input the surveillance video to be classified into a trained surveillance video classification model, and output a classification result of the surveillance video to be classified.

[0045] Specifically, the surveillance video to be classified is obtained, and the surveillance video to be classified is input into the surveillance video classification model whose model accuracy meets the requirements; the surveillance video to be classified is marked with a scene label by the surveillance video classification model, and the classification result of the surveillance video to be classified is output, and the classification result is obtained by grouping according to the relationship between the scene label and the image. In addition, for the surveillance that cannot be grouped, the grouping is completed manually.

[0046] The technical effects that the present invention can bring are as follows:

[0047] (1) Efficiency: Once the model is established, when new monitoring is added to the management, it will be automatically classified according to the rules set in the code.

[0048] (2) Plasticity: When there are new requirements, you can add a scene recognition by modifying the model. Similarly, if you are not satisfied with the existing classification model, you can directly modify the model.

[0049] (3) Easy to iterate: The model is mainly used for classification, without too much calculation and derivation, so the algorithm will not be changed much. During the iteration process, a new model can be directly obtained by adjusting the data set of the training model, and then a new grouping can be obtained.

[0050] (4) Applicable to multiple scenarios: Since the classification rules are determined by the model, as long as there is a data set, it can theoretically be applied to various scenarios.

[0051] The present invention adopts an intelligent way to perform grouping. First, the keywords of the image name and the image content labels are obtained, and then the relationship between these words and scenes (such as agriculture, animal husbandry and fishery, medical care and health) is found. Finally, a model is established through the relationship. In practical applications, after adding new monitoring, it will be automatically classified after being processed by the model.

[0052] Furthermore, if Figure 3 As shown, based on the above-mentioned intelligent grouping method of surveillance videos, the present invention also provides an intelligent grouping system of surveillance videos, wherein the intelligent grouping system of surveillance videos includes:

[0053] The data processing module 51 is used to obtain the names and group names of all monitoring items to be processed, obtain the monitoring content screenshots corresponding to each monitoring item, select some images from all monitoring content screenshots for manual annotation, and add scene labels;

[0054] The model training module 52 is used to obtain the content labels, grouping and name keywords of all images using a third-party tool, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model;

[0055] The video classification module 53 is used to obtain a surveillance video to be classified, input the surveillance video to be classified into a trained surveillance video classification model, and output a classification result of the surveillance video to be classified.

[0056] Furthermore, if Figure 4 As shown, based on the above-mentioned intelligent grouping method and system for surveillance videos, the present invention also provides a terminal accordingly, and the terminal includes a processor 10, a memory 20 and a display 30. Figure 4Only some components of the terminal are shown, but it should be understood that it is not required to implement all of the components shown, and more or fewer components may be implemented instead.

[0057] In some embodiments, the memory 20 may be an internal storage unit of the terminal, such as a hard disk or memory of the terminal. In other embodiments, the memory 20 may also be an external storage device of the terminal, such as a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (SecureDigital, SD) card, a flash card (Flash Card), etc. equipped on the terminal. Further, the memory 20 may also include both an internal storage unit of the terminal and an external storage device. The memory 20 is used to store application software and various types of data installed on the terminal, such as the program code of the installation terminal. The memory 20 may also be used to temporarily store data that has been output or is to be output. In one embodiment, the memory 20 stores a smart grouping program 40 for monitoring video, which can be executed by the processor 10, thereby realizing the smart grouping method for monitoring video in the present application.

[0058] In some embodiments, the processor 10 may be a central processing unit (CPU), a microprocessor or other data processing chip, used to run the program code or process data stored in the memory 20, such as executing the intelligent grouping method of the surveillance video.

[0059] In some embodiments, the display 30 may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, etc. The display 30 is used to display information on the terminal and to display a visual user interface. The components 10-30 of the terminal communicate with each other via a system bus.

[0060] In one embodiment, when the processor 10 executes the intelligent grouping program 40 of the surveillance video in the memory 20, the following steps are implemented:

[0061] Get the names and group names of all monitoring items that need to be processed, and get the monitoring content screenshots corresponding to each monitoring item. Select some images from all monitoring content screenshots for manual annotation and add scene labels.

[0062] Use a third-party tool to obtain the content labels, groupings, and name keywords of all images, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model;

[0063] Obtain a surveillance video to be classified, input the surveillance video to be classified into a trained surveillance video classification model, and output a classification result of the surveillance video to be classified.

[0064] The process of obtaining the names and group names of all monitoring items to be processed, obtaining a screenshot of the monitoring content corresponding to each monitoring item, selecting some images from all the monitoring content screenshots for manual annotation, and adding scene labels specifically includes:

[0065] Get the names and group names of all monitorings that need to be processed, and get the monitoring content screenshots corresponding to each monitoring, where the monitoring content screenshots are named after the corresponding monitoring names;

[0066] Select some images from all surveillance content screenshots for manual annotation and add scene labels. The manually annotated images are used as the standard results of model classification.

[0067] The process of obtaining a screenshot of the monitoring content corresponding to each monitoring includes: receiving the stream, parsing the I frame and saving it as a picture.

[0068] The method of using a third-party tool to obtain the content tags, grouping and name keywords of all images, and training the surveillance video classification model in combination with the scene tags to obtain a trained surveillance video classification model specifically includes:

[0069] Use third-party tools to obtain keywords for content tags, groups, and names of all selected images;

[0070] Organize the scene labels that need to be used, write the correspondence between scene labels and keywords and image content labels, train the surveillance video classification model until it meets the requirements, and obtain a trained surveillance video classification model.

[0071] The step of training the surveillance video classification model until the requirements are met specifically includes:

[0072] After each surveillance video classification model training is completed, it is compared with the standard set obtained by manual grouping;

[0073] If the difference with the standard set is greater than the preset requirement, the correspondence between keywords and tags and scene tags is modified until the classification is reasonable;

[0074] For manually marked unreasonable words, select Delete and add reasonable words to the vocabulary library.

[0075] The vocabulary library includes all keywords and tags.

[0076] The step of obtaining a surveillance video to be classified, inputting the surveillance video to be classified into a trained surveillance video classification model, and outputting a classification result of the surveillance video to be classified specifically includes:

[0077] Obtaining a surveillance video to be classified, and inputting the surveillance video to be classified into a surveillance video classification model whose model accuracy meets the requirements;

[0078] The surveillance video to be classified is marked with a scene label through a surveillance video classification model, and a classification result of the surveillance video to be classified is output, and the classification result is obtained by grouping according to the relationship between the scene label and the image.

[0079] The present invention also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a smart grouping program for surveillance videos, and when the smart grouping program for surveillance videos is executed by a processor, the steps of the smart grouping method for surveillance videos as described above are implemented.

[0080] In summary, the present invention provides a method, system, terminal and storage medium for intelligent grouping of surveillance videos, the method comprising: obtaining the names and group names of all surveillances that need to be processed, and obtaining the surveillance content screenshots corresponding to each surveillance, selecting some images in all surveillance content screenshots for manual annotation, and marking them with scene labels; using a third-party tool to obtain the content labels, grouping and name keywords of all images, and training the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model; obtaining the surveillance videos to be classified that need to be classified, inputting the surveillance videos to be classified into the trained surveillance video classification model, and outputting the classification results of the surveillance videos to be classified. The present invention uses an intelligent way to group surveillance videos, obtains the keywords of image names and image content labels, and finds the relationship between these words and scenes, and finally establishes a classification model through the relationship. After the new surveillance is added, it will be automatically classified after being processed by the classification model.

[0081] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, article or terminal including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or terminal. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, method, article or terminal including the element.

[0082] Of course, those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing related hardware (such as a processor, a controller, etc.) through a computer program, and the program can be stored in a computer-readable storage medium that can be read by a computer, and the program can include the processes of the above-mentioned method embodiments when executed. The computer-readable storage medium can be a memory, a disk, an optical disk, etc.

[0083] It should be understood that the application of the present invention is not limited to the above examples. For ordinary technicians in this field, improvements or changes can be made based on the above description. All these improvements and changes should fall within the scope of protection of the claims attached to the present invention.

Claims

1. A method for intelligent grouping of surveillance videos, characterized in that: The intelligent grouping method of the surveillance video includes: Get the names and group names of all monitoring items that need to be processed, and get the monitoring content screenshots corresponding to each monitoring item. Select some images from all monitoring content screenshots for manual annotation and add scene labels. Use a third-party tool to obtain the content labels, groupings, and name keywords of all images, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model; Obtain a surveillance video to be classified, input the surveillance video to be classified into a trained surveillance video classification model, and output a classification result of the surveillance video to be classified.

2. The intelligent grouping method of surveillance video according to claim 1, characterized in that: The method of obtaining the names and group names of all monitoring items to be processed, obtaining a screenshot of the monitoring content corresponding to each monitoring item, selecting some images from all the monitoring content screenshots for manual annotation, and adding scene labels specifically includes: Get the names and group names of all monitorings that need to be processed, and get the monitoring content screenshots corresponding to each monitoring, where the monitoring content screenshots are named after the corresponding monitoring names; Select some images from all surveillance content screenshots for manual annotation and add scene labels. The manually annotated images are used as the standard results of model classification.

3. The intelligent grouping method for surveillance videos according to claim 2, characterized in that: The process of obtaining a screenshot of the monitoring content corresponding to each monitoring includes: receiving the stream, parsing the I frame and saving it as a picture.

4. The intelligent grouping method of surveillance video according to claim 1, characterized in that: The method of using a third-party tool to obtain the content tags, grouping and name keywords of all images, and training the surveillance video classification model in combination with the scene tags to obtain a trained surveillance video classification model specifically includes: Use third-party tools to obtain keywords for content tags, groups, and names of all selected images; Organize the scene labels that need to be used, write the correspondence between scene labels and keywords and image content labels, train the surveillance video classification model until it meets the requirements, and obtain a trained surveillance video classification model.

5. The intelligent grouping method of surveillance video according to claim 4, characterized in that: The monitoring video classification model is trained until it meets the requirements, specifically including: After each surveillance video classification model training is completed, it is compared with the standard set obtained by manual grouping; If the difference with the standard set is greater than the preset requirement, the correspondence between keywords and tags and scene tags is modified until the classification is reasonable; For manually marked unreasonable words, select Delete and add reasonable words to the vocabulary library.

6. The intelligent grouping method of surveillance video according to claim 5, characterized in that: The vocabulary includes all keywords and tags.

7. The intelligent grouping method of surveillance video according to claim 1, characterized in that: The step of obtaining a surveillance video to be classified, inputting the surveillance video to be classified into a trained surveillance video classification model, and outputting a classification result of the surveillance video to be classified specifically includes: Obtaining a surveillance video to be classified, and inputting the surveillance video to be classified into a surveillance video classification model whose model accuracy meets the requirements; The surveillance video to be classified is marked with a scene label through a surveillance video classification model, and a classification result of the surveillance video to be classified is output, and the classification result is obtained by grouping according to the relationship between the scene label and the image.

8. An intelligent grouping system for monitoring video, characterized in that: The intelligent grouping system of the surveillance video includes: The data processing module is used to obtain the names and group names of all monitoring items that need to be processed, and obtain the monitoring content screenshots corresponding to each monitoring item. Some images are selected from all monitoring content screenshots for manual annotation and are marked with scene labels. The model training module is used to obtain the content labels, grouping and name keywords of all images using third-party tools, and train the surveillance video classification model in combination with the scene labels to obtain a trained surveillance video classification model; The video classification module is used to obtain the surveillance video to be classified, input the surveillance video to be classified into the trained surveillance video classification model, and output the classification result of the surveillance video to be classified.

9. A terminal, characterized in that: The terminal includes: a memory, a processor, and an intelligent grouping program for surveillance videos stored in the memory and executable on the processor. When the intelligent grouping program for surveillance videos is executed by the processor, the steps of the intelligent grouping method for surveillance videos as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a program for intelligent grouping of surveillance videos, and when the program for intelligent grouping of surveillance videos is executed by a processor, the steps of the method for intelligent grouping of surveillance videos as described in any one of claims 1 to 7 are implemented.

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