Research pick: Managing students - "College student management based on machine vision and intelligent monitoring system"

Researchers in China have developed a novel approach to higher education student management that integrates machine vision and intelligent detection technologies. They report details in the International Journal of Information and Communication Technology. The system could address the problems commonly encountered in traditional approaches to management that often cannot cope effectively in meeting the diverse needs of students. Moreover, the system should strengthen safety and improve how a higher-education establishment responds to emergencies.

Yawei Han of Sichuan University in Chengdu Sichuan, China, explains how the new system uses machine vision techniques, including frustum plane calculations and spherical bounding boxes. The system uses the Bresenham algorithm, a computational technique primarily used for drawing lines on a grid-based display, such as a computer screen, to efficiently determine which grid points to plot to form a straight line between two given points. Its use allows for precise conversion of an image into a format that the computer can use for analysis. One important aspect of the new approach is its method for assessing and improving nodes (which are points or elements in a system) using factors like how far away they are and how complex they are. This adaptive approach makes the system more reliable than it would otherwise be.

Overall, the ability of the system to accurately convert vector-based representations of lines into pixel-based raster images for image processing will allow the system to simplify image handling and improve visualization for the identification of students and behaviour.

The new system emphasises inclusivity and responsive communication channels in a way that focuses on the needs of students in a way that conventional approaches have not. Using machine vision and intelligent monitoring technologies can enhance managerial efficiency and bring the focus back to the students. Furthermore, the system highlights the value of utilizing student behaviour data to guide management strategies. Employing various algorithms to model student behaviour, enables targeted interventions and personalized support. There remains the potential to improve sensitivity and database integration. Enhancements in these areas could further strengthen the system’s capabilities and performance.

Han, Y. (2024) ‘College student management based on machine vision and intelligent monitoring system’, Int. J. Information and Communication Technology, Vol. 24, No. 2, pp.228–244.