Top 8 Applications of Computer Vision in the Education Sector


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This article provides an overview of computer vision in education, from improving services and education to increase safety and security at schools and universities.

As AI vision technology is advancing rapidly, more use cases are introduced in the education sector. EdTech applications include personalized learning and vision-based methods to assess the student’s attention and teacher performance.

Recent use cases involve methods to prevent the spread of COVID-19 at schools and other educational institutions such as state colleges and universities.

Computer Vision Technology

Generally, computer vision works in three basic steps: (1) acquiring the video frames from a camera, (2) processing the image with AI algorithms, and (3) understanding the image.

Recently, new deep learning technologies brought great advances to image recognition. State-of-the-art machine learning methods, especially deep learning models, are highly robust and provide accurate real-time object detection and image classification. Hence, AI vision can perform video analytics with the video of common surveillance cameras or webcams.

With the emergence of Edge AI, the combination of edge computing and on-device machine learning, it becomes possible to run deep learning everywhere. On-device AI image processing with Edge ML makes computer vision systems scalable, private, and robust.

Applications of Computer Vision in the Education Sector

  • Application #1: Compliance with Social Distancing
  • Application #2: Mask Detection at Schools
  • Application #3: Parking Management System
  • Application #4: Intrusion Detection in Universities and Schools
  • Application #5: Vandalism Prevention Systems
  • Application #6: Detect Suspicious Unattended Objects
  • Application #7: Facial Emotion Analysis
  • Application #8: Attendance Monitoring

1. Compliance with Social Distancing

Enforcing social distancing has been a key strategy to combat the spread of COVID-19 at public facilities such as schools and universities. Deep learning systems can be used for crowd monitoring to analyze social distancing, identify bottlenecks, and trigger alerts in case of permanent violations.

Social distancing monitoring with vision systems is fully contactless, automated, and easy to implement as no installment of sensors is required, given the video stream of pre-installed security cameras is available. Otherwise, inexpensive surveillance cameras can be installed for large-scale monitoring.

2. Mask Detection at Schools

Masked face detection is a way to monitor compliance and adherence to wearing masks in crowded public places such as universities or schools. Deep learning algorithms automatically detect unmasked people and track mask mandate violations. Privacy-preserving deep learning makes it possible to process all visuals on-device without sending any image to the cloud.

3. Parking Management System

Vision systems are widely used for parking lot occupancy detection at schools or universities. Cameras that are also used for security surveillance provide a video feed that can be used to automatically determine and track the occupancy of multiple parking slots. The information about available parking lots can be visualized in dashboards and sent to third-party systems to provide real-time data to students and teachers. Such systems are highly scalable for large-scale use, and they are used to optimize traffic flows and improve efficiency.

4. Intrusion Detection in Universities and Schools

Deep learning systems can be used with common surveillance cameras to perform perimeter monitoring and detect intruders automatically.

5. Vandalism Prevention Systems

Computer vision based people detection systems can detect suspicious behavior that leads to vandalism and send an alert to on-site personnel. A vandalism prevention system performs person detection to recognize people that enter specific areas.

6. Detect Suspicious Unattended Objects

In traditional video surveillance, personnel has to watch multiple video streams continuously to identify critical situations. Deep learning is used in security applications to perform real-time video analytics using the images of common surveillance cameras. Hence, object detection can identify unattended objects that might pose a threat and trigger an alert for human review.

7. Facial Emotion Analysis

Deep neural networks have been used to recognize student’s emotions from facial emotion analysis. In education, the information of facial emotion recognition can help teachers to adjust their lessons accordingly. Such a method provides a quantifiable, continuous, and automated way to support evaluating a teacher’s service quality. The technology is still new and requires privacy-preserving implementation (using special cameras or Edge AI for on-device machine learning).

8. Attendance Monitoring

Deep face recognition systems can be used in attendance monitoring systems. The video of common inexpensive CCTV cameras can be analyzed with deep learning to automatically detect people and perform face recognition to identify students and register their attendance.

The Bottom Line

As online education is still in its nascent stages of development, the advancement in EdTech can lead to new implementations. Currently, computer vision systems are mostly used for security and safety purposes. However, there is a big potential for Computer Vision in the EdTech industry to improve the quality of educational services.

What’s next

Read more about other use cases in different industries or learn about the deep learning technology behind modern computer vision.

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