Internet of Things video security supervision system based on artificial intelligence

Abstract

The invention belongs to the technical field of security and protection supervision, and particularly relates to an Internet of Things video security and protection supervision system based on artificial intelligence, which comprises an environment analysis module used for analyzing a surrounding environment video acquired in a video acquisition module so as to analyze whether an accident exists in the environment; the road surface analysis module is used for analyzing the road surface video acquired in the video acquisition module so as to analyze whether an accident exists in the road surface or not; and the video playback module is used for intercepting and storing the initial video identified by the environment analysis module and the pavement analysis module. The environment analysis module, the road surface analysis module and the video playback module are arranged, so that accidents can be automatically analyzed, recognized and pre-alarmed when the environment and the road surface are monitored, accident videos can be automatically intercepted, potential safety hazards can be found in advance, workers do not need to manually find the accident videos, and the working efficiency is improved. And the intelligent degree is high.

Description

Technical Field

[0001] The present invention relates to the technical field of security supervision, and in particular to an Internet of Things video security supervision system based on artificial intelligence. Background Art

[0002] The Internet of Things Video Security Supervision System is a video surveillance system based on the Internet of Things technology, which is mainly used to achieve security prevention and intelligent management of specific areas. The system realizes comprehensive monitoring and management functions through the in-depth application of the Internet of Things and sensor networks, and combines human-computer dialogue and logical judgment technology to make the system more flexible and forward-looking.

[0003] When the existing Internet of Things video security monitoring system monitors the road surface and its surrounding environment, if an accident occurs in the area (including but not limited to a fire in the environment or a car accident on the road), personnel are required to replay the monitored video to obtain the video of the accident. In this process, usually after the accident occurs, personnel manually search for it, which not only increases the labor intensity of personnel, but also reduces the efficiency of searching for accident videos, which has limitations. Therefore, an Internet of Things video security monitoring system based on artificial intelligence is invented. Summary of the invention

[0004] To solve the above technical problems, according to one aspect of the present invention, the present invention provides the following technical solutions:

[0005] An IoT video security monitoring system based on artificial intelligence, comprising:

[0006] Video acquisition module, used to collect images of the road surface and its surrounding environment;

[0007] The environment analysis module is used to analyze the surrounding environment video collected by the video acquisition module to analyze whether there is an accident in the environment. If there is an accident, the starting video of the accident will be identified. It can also detect whether there is anyone staying in the restricted area for a long time or climbing over the fence. If so, the starting video will be identified;

[0008] The road surface analysis module is used to analyze the road surface video collected by the video acquisition module to determine whether there is an accident on the road surface. If there is an accident, the starting video of the accident will be identified;

[0009] The video playback module is used to capture and store the starting video identified by the environment analysis module and the road surface analysis module.

[0010] As a preferred solution of the IoT video security monitoring system based on artificial intelligence described in the present invention, the environmental analysis module includes:

[0011] The environment acquisition module is used to acquire the environment image in the video acquired by the video acquisition module;

[0012] An environmental image extraction module is used to extract features from the environmental image collected by the environmental collection module;

[0013] The first environment storage module is used to store various accident start images in the environment;

[0014] The second environment storage module is used to store various accident end images in the environment.

[0015] As a preferred solution of the artificial intelligence-based IoT video security monitoring system described in the present invention, the environmental analysis module further includes:

[0016] An environment comparison module, used to compare the features extracted by the environment image extraction module with the images stored by the first environment storage module and the second environment storage module;

[0017] The environment judgment module is used to judge whether the features extracted by the environment image extraction module are similar in the first environment storage module and the second environment storage module. If they are similar in the first environment storage module, the start video of the accident will be found; if they are similar in the second environment storage module, the end video of the accident will be found;

[0018] The first alarm module is used to generate an alarm when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module.

[0019] As a preferred solution of the artificial intelligence-based IoT video security monitoring system described in the present invention, the environmental analysis module further includes:

[0020] A first environment marking module is used to mark the video as the start video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module;

[0021] The second environment marking module is used to mark the video as the end video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the second environment storage module.

[0022] As a preferred solution of the artificial intelligence-based Internet of Things video security monitoring system described in the present invention, the road surface analysis module includes:

[0023] A road surface acquisition module is used to acquire road surface images in the video acquired by the video acquisition module;

[0024] The road surface image extraction module is used to extract features from the road surface image collected by the road surface collection module.

[0025] As a preferred solution of the artificial intelligence-based Internet of Things video security monitoring system described in the present invention, the road surface analysis module also includes:

[0026] The first road surface storage module is used to store various accident start images on the road surface;

[0027] The second road surface storage module is used to store various accident completion images on the road surface.

[0028] As a preferred solution of the artificial intelligence-based Internet of Things video security monitoring system described in the present invention, the road surface analysis module also includes:

[0029] A road surface comparison module, used to compare the features extracted by the road surface image extraction module with the images stored in the first road surface storage module and the second road surface storage module;

[0030] A road surface judgment module is used to judge whether the features extracted by the road surface image extraction module are similar in the first road surface storage module and the second road surface storage module. If they are similar in the first road surface storage module, the start video of the accident will be found; if they are similar in the second road surface storage module, the end video of the accident will be found;

[0031] The first alarm module is used to generate an alarm when the features extracted by the road surface image extraction module are similar to the images stored in the first road surface storage module.

[0032] As a preferred solution of the artificial intelligence-based Internet of Things video security monitoring system described in the present invention, the road surface analysis module also includes:

[0033] A first road marking module is used to mark the video as the start video of the accident when the features extracted by the road image extraction module are similar to the images stored in the first road storage module;

[0034] The second road surface marking module is used to mark the video as the end video of the accident when the features extracted by the road surface image extraction module are similar to the images stored in the second road surface storage module.

[0035] As a preferred solution of the IoT video security monitoring system based on artificial intelligence described in the present invention, the video playback module includes:

[0036] An acquisition module, used to acquire the video between the first environment marking module and the second environment marking module and the video between the first road surface marking module and the second road surface marking module;

[0037] The range expansion module is used to expand the video between the first environmental marking module and the second environmental marking module and the video between the first road surface marking module and the second road surface marking module to further ensure the integrity of the accident video.

[0038] As a preferred solution of the artificial intelligence-based IoT video security monitoring system described in the present invention, the video playback module further includes:

[0039] An interception module, used for intercepting the enlarged video;

[0040] The storage module is used to store the captured video for easy viewing by personnel.

[0041] Compared with existing technologies:

[0042] By setting up the environment analysis module, road surface analysis module and video playback module, it is possible to automatically analyze and identify accidents, issue pre-alarms and automatically capture accident videos when monitoring the environment and road surface. This not only enables early detection of safety hazards, but also eliminates the need for personnel to manually search for accident videos, with a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0044] In order to make the objectives, technical solutions and advantages of the present invention more clear, the embodiments of the present invention will be further described in detail below with reference to the accompanying drawings.

[0045] The present invention provides an IoT video security monitoring system based on artificial intelligence. Figure 1 ;

[0046] It includes: a video acquisition module, which is used to collect images of the road surface and its surrounding environment; an environmental analysis module, which is used to analyze the surrounding environment video collected by the video acquisition module to analyze whether there is an accident in the environment. If there is an accident, the starting video of the accident will be identified; it can also detect whether there is someone staying in a restricted area for a long time or climbing over a fence in violation of regulations. If so, the starting video will be identified; a road surface analysis module, which is used to analyze the road surface video collected by the video acquisition module to analyze whether there is an accident in the road surface. If there is an accident, the starting video of the accident will be identified; a video playback module, which is used to intercept and store the starting videos identified by the environmental analysis module and the road surface analysis module.

[0047] The environment analysis module includes: an environment acquisition module, which is used to acquire environment images in the video acquired by the video acquisition module; an environment image extraction module, which is used to extract features from the environment images acquired by the environment acquisition module; a first environment storage module, which is used to store various accident start images in the environment; a second environment storage module, which is used to store various accident end images in the environment; an environment comparison module, which is used to compare the features extracted by the environment image extraction module with the images stored in the first environment storage module and the second environment storage module; an environment judgment module, which is used to judge whether the features extracted by the environment image extraction module are similar in the first environment storage module and the second environment storage module, and if so, If there is similarity, the start video of the accident will be found; if there is similarity in the second environment storage module, the end video of the accident will be found; the first alarm module is used to sound an alarm when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module; the first environment marking module is used to mark the video as the start video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module; the second environment marking module is used to mark the video as the end video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the second environment storage module.

[0048] The road surface analysis module includes: a road surface acquisition module, which is used to acquire road surface images in the video acquired by the video acquisition module; a road surface image extraction module, which is used to extract features from the road surface images acquired by the road surface acquisition module; a first road surface storage module, which is used to store various accident start images in the road surface; a second road surface storage module, which is used to store various accident end images in the road surface; a road surface comparison module, which is used to compare the features extracted by the road surface image extraction module with the images stored in the first road surface storage module and the second road surface storage module; a road surface judgment module, which is used to judge whether the features extracted by the road surface image extraction module are similar in the first road surface storage module and the second road surface storage module, and if they are in the first road surface storage module, If there is similarity, the start video of the accident will be found, and if there is similarity in the second road surface storage module, the end video of the accident will be found; the first alarm module is used to sound an alarm when the features extracted by the road surface image extraction module are similar to the images stored in the first road surface storage module; the first road surface marking module is used to mark the video as the start video of the accident when the features extracted by the road surface image extraction module are similar to the images stored in the first road surface storage module; the second road surface marking module is used to mark the video as the end video of the accident when the features extracted by the road surface image extraction module are similar to the images stored in the second road surface storage module.

[0049] The video playback module includes: an acquisition module, which is used to acquire the video between the first environmental marking module and the second environmental marking module and the video between the first road surface marking module and the second road surface marking module; a range expansion module, which is used to expand the video between the first environmental marking module and the second environmental marking module and the video between the first road surface marking module and the second road surface marking module to further ensure the integrity of the accident video; an interception module, which is used to intercept the expanded video; and a storage module, which is used to store the intercepted video for personnel to view.

[0050] When used, the specific steps are as follows:

[0051] Step 1: Collect the road surface and its surrounding environment through the video acquisition module;

[0052] Step 2: The environment image in the video collected by the video collection module is collected by the environment collection module. After the collection, the environment image extraction module will extract features of the environment image collected by the environment collection module. After the extraction, the features extracted by the environment image extraction module will be compared with the images stored in the first environment storage module through the environment comparison module. After the comparison, the environment judgment module will be used to determine whether the features extracted by the environment image extraction module are similar in the first environment storage module. If they are similar in the first environment storage module, the start video of the accident will be found and marked by the first environment marking module. At the same time, the first alarm module will be used to alarm. After the alarm, the above steps will be repeated. If the environment judgment module determines that the features extracted by the environment image extraction module are similar in the second environment storage module, the end video of the accident will be found and the second environment marking module will be marked. For example, when the environment judgment module determines that smoke occurs in the environment, the first environment marking module will be used to mark it. After marking, the environment judgment module will continue to be used for judgment until the smoke or fire ends, and the second environment marking module will be used for marking.

[0053] Step 3: The road surface image in the video collected by the video collection module is collected by the road surface collection module. After the collection, the road surface image extraction module will extract features from the road surface image collected by the road surface collection module. After the extraction, the features extracted by the road surface image extraction module will be compared with the images stored in the first road surface storage module by the road surface comparison module. After the comparison, the road surface judgment module will be used to judge whether the features extracted by the road surface image extraction module are similar in the first road surface storage module. If they are similar in the first road surface storage module, the start video of the accident will be found and marked by the first road surface marking module. At the same time, the first alarm module will be used to alarm. After the alarm, the above steps will be repeated. If the road surface judgment module determines that the features extracted by the road surface image extraction module are similar in the second road surface storage module, the end video of the accident will be found and the second road surface marking module will be marked. For example, when the road surface judgment module determines that a car collision occurs on the road surface, it will be marked by the first road surface marking module. After marking, the road surface judgment module will continue to judge until the colliding car is no longer within the monitoring range and is marked by the second road surface marking module.

[0054] Step 4: The video between the first environment marking module and the second environment marking module and the video between the first road surface marking module and the second road surface marking module are acquired through the acquisition module. After acquisition, the video between the first environment marking module and the second environment marking module and the video between the first road surface marking module and the second road surface marking module are expanded forward and backward through the range expansion module to further ensure the integrity of the accident video. After expansion, the expanded video is intercepted by the interception module. After interception, the intercepted video is stored through the storage module for easy viewing by personnel.

[0055] Although the present invention has been described above with reference to the embodiments, various modifications may be made thereto and parts thereof may be replaced by equivalents without departing from the scope of the present invention. In particular, as long as there is no structural conflict, the various features in the embodiments disclosed in the present invention may be used in combination with each other in any manner, and the fact that these combinations are not exhaustively described in this specification is only for the sake of omitting space and saving resources. Therefore, the present invention is not limited to the specific embodiments disclosed herein, but includes all technical solutions falling within the scope of the claims.

Claims

1. An IoT video security monitoring system based on artificial intelligence, characterized in that: include: Video acquisition module, used to collect images of the road surface and its surrounding environment; The environment analysis module is used to analyze the surrounding environment video collected by the video acquisition module to analyze whether there is an accident in the environment. If there is an accident, the starting video of the accident will be identified. It can also detect whether there is anyone staying in the restricted area for a long time or climbing over the fence. If so, the starting video will be identified; The road surface analysis module is used to analyze the road surface video collected by the video acquisition module to determine whether there is an accident on the road surface. If there is an accident, the starting video of the accident will be identified; A video playback module, used to capture and store the starting video identified by the environment analysis module and the road surface analysis module; The environmental analysis module also includes: A first environment marking module is used to mark the video as the start video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module; The second environment marking module is used to mark the video as the end video of the accident when the features extracted by the environment image extraction module are similar to the images stored in the second environment storage module; The road surface analysis module includes: A road surface acquisition module is used to acquire road surface images in the video acquired by the video acquisition module; A road surface image extraction module is used to extract features from the road surface image collected by the road surface collection module; The road surface analysis module also includes: The first road surface storage module is used to store various accident start images on the road surface; The second road surface storage module is used to store various accident completion images on the road surface.

2. According to the artificial intelligence-based Internet of Things video security monitoring system according to claim 1, it is characterized in that: The environmental analysis module includes: The environment acquisition module is used to acquire the environment image in the video acquired by the video acquisition module; An environmental image extraction module is used to extract features from the environmental image collected by the environmental collection module; The first environment storage module is used to store various accident start images in the environment; The second environment storage module is used to store various accident end images in the environment.

3. According to the artificial intelligence-based Internet of Things video security monitoring system of claim 1, it is characterized in that: The environmental analysis module also includes: An environment comparison module, used to compare the features extracted by the environment image extraction module with the images stored by the first environment storage module and the second environment storage module; The environment judgment module is used to judge whether the features extracted by the environment image extraction module are similar in the first environment storage module and the second environment storage module. If they are similar in the first environment storage module, the start video of the accident will be found; if they are similar in the second environment storage module, the end video of the accident will be found; The first alarm module is used to generate an alarm when the features extracted by the environment image extraction module are similar to the images stored in the first environment storage module.

4. According to the artificial intelligence-based Internet of Things video security monitoring system of claim 1, it is characterized in that: The road surface analysis module also includes: A road surface comparison module, used to compare the features extracted by the road surface image extraction module with the images stored in the first road surface storage module and the second road surface storage module; A road surface judgment module is used to judge whether the features extracted by the road surface image extraction module are similar in the first road surface storage module and the second road surface storage module. If they are similar in the first road surface storage module, the start video of the accident will be found; if they are similar in the second road surface storage module, the end video of the accident will be found; The first alarm module is used to generate an alarm when the features extracted by the road surface image extraction module are similar to the images stored in the first road surface storage module.

5. According to the artificial intelligence-based Internet of Things video security monitoring system of claim 4, it is characterized in that: The road surface analysis module also includes: A first road marking module is used to mark the video as the start video of the accident when the features extracted by the road image extraction module are similar to the images stored in the first road storage module; The second road surface marking module is used to mark the video as the end video of the accident when the features extracted by the road surface image extraction module are similar to the images stored in the second road surface storage module.

6. The artificial intelligence-based Internet of Things video security monitoring system according to claim 5 is characterized in that: The video playback module includes: An acquisition module, used to acquire the video between the first environment marking module and the second environment marking module and the video between the first road surface marking module and the second road surface marking module; The range expansion module is used to expand the video between the first environmental marking module and the second environmental marking module and the video between the first road surface marking module and the second road surface marking module to further ensure the integrity of the accident video.

7. The IoT video security monitoring system based on artificial intelligence according to claim 1 is characterized in that: The video playback module also includes: An interception module, used for intercepting the enlarged video; The storage module is used to store the captured video for easy viewing by personnel.

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