Processing method and system for monitoring video artificial intelligence analysis

Abstract

The invention discloses a processing method and system for monitoring video artificial intelligence analysis, and relates to the field of video intelligent analysis. The method comprises the following steps: step 1, a video monitoring module records student activities and uploads a monitoring video to a processing terminal, and the processing terminal preprocesses an acquired monitoring video image, including noise removal and image brightness and contrast adjustment; according to the invention, through intelligent analysis of the monitoring video, a large amount of monitoring data can be automatically processed, manual intervention is reduced, management efficiency is improved, burden of monitoring personnel is reduced, the error rate of manual monitoring is reduced, monitoring pictures can be analyzed in real time, abnormal behaviors or potential security threats can be detected, security personnel can be assisted in timely processing, and the safety of the security personnel is improved. Meanwhile, behavior trends of students and faculty and employees can be analyzed, and violence and illegal behaviors of the campus can be further reduced in cooperation with an alarm unit.

Description

Technical Field

[0001] The present invention relates to the technical field of video intelligent analysis, and in particular to a processing method and system for artificial intelligence analysis of surveillance videos. Background Art

[0002] Campus safety in primary and secondary schools is related to the normal development of school education and teaching, and to the stability of schools and society. At present, the construction of safe campuses has become an important aspect of strengthening school management. The connotation and extension of campus safety are very rich, mainly involving personal safety. In general, campus safety is the comprehensive safety of people, finances, materials, and campus culture. Schools are places for educating people. Creating a quiet, harmonious, healthy, and safe learning and growth environment for students is the common wish of all teachers, students, and staff, and it is also a requirement for the harmonious development of the whole society. The monitoring system is applied in the campus to obtain various videos, sounds and other information in the monitoring area, to monitor and record the process of sudden abnormal events in a timely manner, and to provide strong support for the timely handling of emergency events. However, the existing camera system requires long-term duty by on-duty personnel, and can only mechanically record the events that occur in real time, and cannot perform intelligent analysis of the video images; therefore, once an emergency occurs, the on-duty personnel are still required to notify the relevant responsible persons of the school. If the on-duty personnel do not discover the emergency and fail to report the alarm in time, it will result in improper accident handling and cause major safety accidents.

[0003] To this end, we provide a processing method and system for artificial intelligence analysis of surveillance videos to solve the above problems. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a processing method and system for artificial intelligence analysis of surveillance videos, which solves the problem that the existing surveillance system requires on-duty personnel to perform manual monitoring and analysis and cannot respond to sudden emergencies in a timely manner.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a processing method for artificial intelligence analysis of surveillance videos, comprising the following steps; Step 1: The video monitoring module records student activities and uploads the monitoring video to the processing terminal, which pre-processes the collected monitoring video images, including removing noise and adjusting image brightness and contrast; Step 2: Use artificial intelligence algorithms to identify and analyze the students in the video images, confirm the students' facial information, identity information, and current activities, compare the students' activities with the database, and classify them into regular behaviors and special behaviors; Step 3: The special behavior is graded from level one to level four, and different reminder information is sent to relevant personnel and alarm units according to the judgment results. The alarm unit sends an audible and visual alarm reminder for the ongoing special behavior, and the reminder information level is downward compatible; Step 4: Mark students who are detected to have special behaviors as special students, locate the special students through target tracking technology, determine their positions and movement trajectories, and monitor their activities on campus in real time.

[0006] Furthermore, in step 2, the artificial intelligence algorithm is a convolutional neural network, a support vector machine, a Kalman filter or a Gaussian mixture model, and the preferred artificial intelligence algorithm is a convolutional neural network.

[0007] Furthermore, in step 2, conventional behaviors include walking, running, playing, and eating, and special behaviors include fighting, climbing, vandalism, bullying classmates, and stealing.

[0008] Furthermore, in step 3, the special behaviors are graded as follows: special behaviors that do not cause damage to campus facilities are judged as level one, special behaviors that cause damage to campus facilities but do not cause physical harm to students are judged as level two, special behaviors that cause certain damage to campus facilities and cause minor physical injuries or emotional distress to students are judged as level three, and special behaviors that cause serious damage to campus facilities and cause multiple students to be injured or require medical treatment are judged as level four.

[0009] Furthermore, level one and level two special behaviors send alarm information to the class teacher and parents of relevant students, and level three and level four special behaviors send alarm information to school senior management and parents of relevant students.

[0010] Furthermore, in step 4, the real-time monitoring time is extended according to the special behavior records of special students. The basic monitoring time without special behavior records is 48 hours, and the monitoring time is increased by 48 hours for each additional special behavior record.

[0011] A processing system for artificial intelligence analysis of surveillance videos, comprising a processing terminal, a video surveillance module, an analysis module and an alarm unit; The processing terminal is used to judge the student behavior analysis uploaded by the analysis module and implement control and data transmission instructions between multiple modules; The video surveillance module is used to monitor various areas of the school and can observe the activities of students, faculty and staff, and visitors in real time; The analysis module is used to analyze the pre-processed surveillance video images, identify the identity information of students in the surveillance video and analyze the students' activities and behaviors; The alarm unit can issue an audible and visual alarm to remind the ongoing special behavior according to the command issued by the processing terminal; The video monitoring module and the alarm unit are synchronously installed in the campus, and the analysis module is installed inside the processing terminal.

[0012] Furthermore, it also includes a database, which includes an information database and a behavior database. The information database is used to store student information of each class, course information and teacher information of each class, and the behavior database is used to store behavioral characteristics of students' regular behaviors and special behaviors, as well as special behavior records.

[0013] Furthermore, the video monitoring module is preferably a camera, and the alarm unit is preferably a buzzer and an alarm light.

[0014] Furthermore, the video monitoring module also includes an adjustment unit, and the adjustment unit can adjust the monitoring direction and monitoring viewing angle of the video monitoring module.

[0015] Compared with the prior art, the processing method and system for artificial intelligence analysis of surveillance video have the following beneficial effects: 1. The present invention can not only automatically process a large amount of monitoring data, reduce manual intervention, improve management efficiency, reduce the burden on monitoring personnel, and reduce the error rate of manual monitoring by performing intelligent analysis on monitoring videos, but also can analyze monitoring images in real time, detect abnormal behavior or potential security threats, and assist security personnel in timely handling. At the same time, it can also analyze the behavioral trends of students and faculty members, and cooperate with the alarm unit to further reduce the occurrence of campus violence and illegal behavior.

[0016] 2. By grading special behaviors, the present invention can not only take more appropriate measures when handling incidents according to the severity of different levels of incidents, handle them in a targeted manner, and ensure agile and efficient response, but also can timely discover and prevent potential safety hazards and problems, strengthen campus safety management awareness and control measures, and help improve the campus safety level. At the same time, by grading campus incidents, it can help to better manage risks on campus, respond to various incidents quickly and accurately, and reduce the impact of incidents on campus order and safety.

[0017] 3. Through follow-up monitoring of special students, the present invention can not only help schools track students' behavior and performance, understand their progress in correcting mistakes and adjusting behaviors, discover problems in time and intervene, but also provide continuous support and guidance to students who have made mistakes, help them overcome difficulties, improve behaviors, and achieve good development. At the same time, regular monitoring can help schools promptly discover and prevent students who have made mistakes from engaging in inappropriate behaviors again, strengthen the control and attention to their behaviors, and effectively avoid the occurrence of vicious cycles.

[0018] Other advantages, objectives and features of the present invention will be set forth in part in the following description and, in part, will be apparent to those skilled in the art based on an examination of the following or may be taught from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a flow chart of the processing method of the surveillance video artificial intelligence analysis of the present invention; Figure 2 Processing system diagram for artificial intelligence analysis of surveillance video in the present invention. DETAILED DESCRIPTION

[0020] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention. Example 1

[0021] See also Figure 1 ,The present invention provides a technical solution: a processing method for artificial intelligence analysis of surveillance videos, comprising the following steps; Step 1: The video surveillance module records student activities and uploads the surveillance video to the processing terminal. The processing terminal preprocesses the collected surveillance video images, including removing noise and adjusting image brightness and contrast.

[0022] Step 2: Use artificial intelligence algorithms to identify and analyze the students in the video images, confirm the students' facial information, identity information and current activities, compare the students' activities with the database, and divide them into regular behaviors and special behaviors; the artificial intelligence algorithm is a convolutional neural network, a support vector machine, a Kalman filter or a Gaussian mixture model, and the preferred artificial intelligence algorithm is a convolutional neural network; regular behaviors include walking, running, playing games and eating, and special behaviors include fighting, climbing, destroying public property, bullying classmates and stealing.

[0023] Step 3: The special behaviors are graded from level one to level four, and different reminder information is sent to relevant personnel and alarm units according to the judgment results. The alarm unit sends out sound and light alarm reminders for the ongoing special behaviors, and the reminder information is downward compatible. The graded judgment of special behaviors is as follows: special behaviors that do not cause damage to campus facilities are judged as level one, special behaviors that cause damage to campus facilities but do not cause physical harm to students are judged as level two, special behaviors that cause certain damage to campus facilities and cause minor physical harm or emotional distress to students are judged as level three, and special behaviors that cause serious damage to campus facilities and cause multiple students to be injured or require medical treatment are judged as level four. Level one and level two special behaviors In order to send alarm information to the class teacher and the parents of relevant students, the third and fourth level special behaviors send alarm information to the school senior management and the parents of relevant students; by grading special behaviors, the present invention can not only take more corresponding measures when handling events according to the severity of events at different levels, and handle them in a targeted manner to ensure agile and efficient response, but also can timely discover and prevent potential safety hazards and problems, strengthen campus safety management awareness and control measures, and help improve the campus safety level. At the same time, by grading campus events, it can help to better manage risks on campus, respond to various events quickly and accurately, and reduce the impact of events on campus order and safety.

[0024] Step 4, mark the students detected with special behaviors as special students, locate the special students through target tracking technology, determine the position and movement trajectory of the special students, and monitor the activities of the special students on campus in real time; according to the special behavior records of special students, extend the real-time monitoring time, the basic monitoring time without special behavior records is 48 hours, and the monitoring time is increased by 48 hours for each additional special behavior record; the present invention can not only help schools track students' behavior and performance, understand their progress in correcting mistakes and adjusting behaviors, discover problems in time and intervene, but also provide continuous support and guidance to students who have made mistakes, help them overcome difficulties, improve behaviors, and achieve good development, while regular monitoring can help schools to promptly discover and prevent students who have made mistakes from engaging in inappropriate behaviors again, strengthen the control and attention to their behaviors, and effectively avoid the occurrence of a vicious cycle.

[0025] A processing system for artificial intelligence analysis of surveillance videos comprises a processing terminal, a video surveillance module, an analysis module and an alarm unit; the processing terminal is used for judging the student behavior analysis uploaded by the analysis module, and realizing control and data transmission instructions between multiple modules at the same time; the video surveillance module is used for monitoring various areas of a school, and can observe the activities and behaviors of students, faculty and staff and visitors in real time; the analysis module is used for analyzing the pre-processed surveillance video images, identifying the student identity information and the student activity and behavior analysis in the surveillance video; the alarm unit can issue an audible and visual alarm reminder for the ongoing special behavior according to the command issued by the processing terminal; the video surveillance module and the alarm unit are synchronously installed in the campus, and the analysis module is installed inside the processing terminal; the present invention can not only automatically process a large amount of surveillance data, reduce manual intervention, improve management efficiency, reduce the burden of monitoring personnel, and reduce the error rate of manual monitoring by performing intelligent analysis on surveillance videos, but also can analyze the surveillance images in real time, detect abnormal behaviors or potential security threats, and assist security personnel in timely handling, and can also analyze the behavioral trends of students and faculty and staff, and cooperate with the alarm unit to further reduce the occurrence of campus violence and illegal behaviors.

[0026] It also includes a database, which includes an information database and a behavior database. The information database is used to store student information, course information and teacher information of each class, and the behavior database is used to store behavioral characteristics of students' regular behaviors and special behaviors, as well as special behavior records.

[0027] The video monitoring module is preferably a camera, and the alarm unit is preferably a buzzer and an alarm light.

[0028] The video monitoring module also includes an adjustment unit, which can adjust the monitoring direction and monitoring viewing angle of the video monitoring module. Example 2

[0029] Based on the first embodiment, a processing system for artificial intelligence analysis of surveillance videos also includes a social module, which uses social network analysis methods to analyze the communication relationships and social network structures between students, identify close relationships and group dynamics between students, and mark long-term independent students as autistic students. The processing terminal then sends the autistic student information to the teaching teacher, who will guide the student and locate the autistic student in real time during the guidance period.

[0030] Guidance can help autistic students understand their own emotional state and emotions, guide them to learn to manage their emotions, and help improve their emotional self-control. Through real-time monitoring, we can pay more personalized attention to their learning and living conditions and provide them with more considerate support and help. At the same time, real-time monitoring can help schools promptly discover the troubles and safety hazards they may face, intervene and protect them, and enhance their sense of security. Example 3

[0031] Based on Example 1, a processing system for artificial intelligence analysis of surveillance video also includes an access control module. The video surveillance module collects the identity information of people entering and leaving, the processing terminal compares and identifies the personnel information, and issues instructions to the access control module. The access control module controls the opening / closing of the access control.

[0032] By using artificial intelligence to analyze the identity information of people entering and leaving the system, it is not only possible to effectively control the permissions for entering and leaving the access control system and improve the security and accuracy of the access control system, but also to achieve automated monitoring, intelligent identification and rapid response, thereby improving the experience and convenience of people entering and leaving the system. There is no need to carry additional access cards or passwords, making entry and exit more convenient. At the same time, through artificial intelligence analysis, abnormal situations and security risks can be discovered in a timely manner, such as the entry of unauthorized personnel, thereby enhancing security prevention capabilities and protecting the safety of the campus and students. Example 4

[0033] Based on the first embodiment, a processing system for artificial intelligence analysis of surveillance video also includes a flame detection unit and a vehicle identification unit. The flame detection unit is used to identify the flame area in the video data collected by the video surveillance module. When the flame area reaches a set value, the flame detection unit sends an alarm message to the processing terminal, and the processing terminal identifies the alarm level based on the received information; the vehicle identification unit is used to compare the license plate image of the vehicle entering the campus area with the license plate image information in the database.

[0034] By using artificial intelligence technology to identify flames, not only can we quickly detect and identify fire risks, discover fire hazards in advance, ensure timely emergency prevention measures, and reduce fire losses, but we can also achieve real-time monitoring and alarm of flames and fires, strengthen safety precautions, and ensure the safety of people and property; at the same time, by using artificial intelligence technology to identify vehicles, we can achieve intelligent management and monitoring of vehicle entry and exit, improve traffic management efficiency, and reduce violations and campus safety.

[0035] The present invention provides a processing method for artificial intelligence analysis of surveillance videos, comprising the following steps: Step 1: The video monitoring module records student activities and uploads the monitoring video to the processing terminal, which pre-processes the collected monitoring video images, including removing noise and adjusting image brightness and contrast; Step 2: Use artificial intelligence algorithms to identify and analyze the students in the video images, confirm the students' facial information, identity information, and current activities, compare the students' activities with the database, and classify them into regular behaviors and special behaviors; Step 3: The special behavior is graded from level one to level four, and different reminder information is sent to relevant personnel and alarm units according to the judgment results. The alarm unit sends an audible and visual alarm reminder for the ongoing special behavior, wherein the reminder information is downward compatible; Step 4: Mark students who are detected to have special behaviors as special students, locate the special students through target tracking technology, determine their positions and movement trajectories, and monitor their activities on campus in real time.

[0036] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the present invention can be implemented in other specific forms without departing from the spirit or essential features of the present invention. Therefore, the embodiments should be considered exemplary and non-restrictive in all respects, and the scope of the present invention is defined by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims be included in the present invention.

Claims

1. A method for artificial intelligence analysis of surveillance video, characterized in that: The steps include: Step 1: The video monitoring module records student activities and uploads the monitoring video to the processing terminal, which pre-processes the collected monitoring video images, including removing noise and adjusting image brightness and contrast; Step 2: Use artificial intelligence algorithms to identify and analyze the students in the video images, confirm the students' facial information, identity information, and current activities, compare the students' activities with the database, and classify them into regular behaviors and special behaviors; Step 3: The special behavior is graded from level one to level four, and different reminder information is sent to relevant personnel and alarm units according to the judgment results. The alarm unit sends an audible and visual alarm reminder for the ongoing special behavior, and the reminder information level is downward compatible; Step 4: Mark students who are detected to have special behaviors as special students, locate the special students through target tracking technology, determine their positions and movement trajectories, and monitor their activities on campus in real time.

2. The method for artificial intelligence analysis of surveillance video according to claim 1, characterized in that: In step 2, the artificial intelligence algorithm is any one of a convolutional neural network, a support vector machine, a Kalman filter or a Gaussian mixture model.

3. The method for artificial intelligence analysis of surveillance video according to claim 1, characterized in that: In step 2, routine behaviors include walking, running, playing, and eating, and special behaviors include fighting, climbing, vandalism, bullying classmates, and stealing.

4. The method for artificial intelligence analysis of surveillance video according to claim 1, characterized in that: In step 3, special behaviors are graded as follows: special behaviors that do not cause damage to campus facilities are graded as level one; special behaviors that cause damage to campus facilities but do not cause physical harm to students are graded as level two; special behaviors that cause certain damage to campus facilities and cause minor physical harm or emotional distress to students are graded as level three; special behaviors that cause serious damage to campus facilities and cause multiple students to be injured or require medical treatment are graded as level four.

5. The method for artificial intelligence analysis of surveillance video according to claim 4, characterized in that: Level 1 and 2 special behaviors send alarm information to the class teacher and parents of relevant students, while level 3 and 4 special behaviors send alarm information to school senior management and parents of relevant students.

6. The method for artificial intelligence analysis of surveillance video according to claim 1, characterized in that: In step 4, the real-time monitoring time is extended according to the special behavior records of special students. The basic monitoring time without special behavior records is 48 hours. For each additional special behavior record, the monitoring time is increased by 48 hours.

7. A surveillance video artificial intelligence analysis processing system, characterized in that: It includes a processing terminal, a video monitoring module, an analysis module and an alarm unit; The processing terminal is used to judge the student behavior analysis uploaded by the analysis module and implement control and data transmission instructions between multiple modules; The video surveillance module is used to monitor various areas of the school and can observe the activities of students, faculty and staff, and visitors in real time; The analysis module is used to analyze the pre-processed surveillance video images, identify the identity information of students in the surveillance video and analyze the students' activities and behaviors; The alarm unit can issue an audible and visual alarm to remind the ongoing special behavior according to the command issued by the processing terminal; The video monitoring module and the alarm unit are synchronously installed in the campus, and the analysis module is installed inside the processing terminal.

8. The processing system for artificial intelligence analysis of surveillance video according to claim 7, characterized in that: It also includes a database, which includes an information database and a behavior database. The information database is used to store student information of each class, course information and teacher information of each class, and the behavior database is used to store behavioral characteristics of students' regular behaviors and special behaviors, as well as special behavior records.

9. The processing system for artificial intelligence analysis of surveillance video according to claim 7, characterized in that: The video monitoring module is a camera, and the alarm unit is a buzzer and an alarm light.

10. The processing system for artificial intelligence analysis of surveillance video according to claim 7, characterized in that: The video monitoring module also includes an adjustment unit, which can adjust the monitoring direction and monitoring viewing angle of the video monitoring module.

Images