Campus safety monitoring and early warning management system based on artificial intelligence

Abstract

The invention discloses a campus safety monitoring and early warning management system based on artificial intelligence. The system comprises a campus area division module, a GPS positioning module, astudent activity image acquisition module, an image preprocessing module, a student dangerous behavior information base, a behavior comparison and identification module, a duration statistics module,a danger coefficient statistics module, a total monitoring platform and an early warning module. According to the invention, outdoor areas in a campus are divided; student activity images in all thesub-regions are collected; meanwhile, student behaviors in the collected student activity images are compared to identify whether the behaviors are dangerous behaviors or not; the main monitoring platform arranges related personnel to go to the site for processing the identified dangerous behaviors, and graded early warning is carried out to avoid processing delay; intelligent monitoring management on campus safety is achieved, the defects of an existing campus video monitoring patrol system are effectively overcome, the probability of dangerous behaviors of students is greatly reduced, and the safety of the students is guaranteed.

Description

technical field [0001] The invention belongs to the technical field of campus safety management, and relates to an artificial intelligence-based campus safety monitoring and early warning management system. Background technique [0002] The campus is a place where a large number of students gather. A large number of students gather may cause friction. Dangerous behaviors such as student fighting, chasing and scrambling often occur, causing students to be injured. Such similar campus safety problems occur frequently and seriously affect the normal education of the school. Teaching order and the healthy development of students, so the safety monitoring of the campus is very important. In order to reduce the occurrence of dangerous behaviors of students, it is necessary to inspect every corner of the campus. Traditional manual inspections are inefficient and prone to omissions and waste A large amount of manpower, in order to reduce the pressure of inspection management, the cu...

AI Technical Summary

Problems solved by technology

[0002] The campus is a place where a large number of students gather. A large number of students gather may cause friction. Dangerous behaviors such as student fighting, chasing and scrambling often occur, causing students to be injured. Such similar campus safety problems occur frequently and seriously affect the normal education of the school. Teaching order and the healthy development of students, so the safety monitoring of the campus is very important. In order to reduce the occurrence of dangerous behaviors of students, it is necessary to inspect every corner of the campus. Traditional manual inspections are inefficient and prone to omissions and waste A large amount of manpower, in order to reduce the pressure of inspection management, the current campus security inspections use video surveillance technology, install monitoring probes at all angles of the campus, and reduce the occurrence of students' dangerous behaviors by checking the monitoring situation in every corner of the campus at the general monitoring center, but at present The current campus monitoring system has the following disadvantages:

[0003] 1. The general monitoring center monitors and identifies whether the behavior of students in the surveillance images in every corner of the campus is a dangerous behavior through manual inspection and identification. The identification efficiency is low, and the naked eye identification is prone to omissions, and the degree of intelligence is not high;

[0004] 2. When the general monitoring center monitors that there is a dangerous behavior of students in a certain corner of the campus, it is necessary to send relevant personnel to deal with it. However, it takes a certain amount of time to go from the general monitoring center to this corner. During this period, there are no intermediate early warning measures, which may easily lead to processing delay

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