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.
Monitor social distancing between people, identify high-risk areas and non-compliance.
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.
Automatically detect unmasked people in public spaces or indoors.
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.
Complete application to detect vehicles with Computer Vision and Deep Learning
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.
Detect intrusion events in pre-defined areas by identifying the target’s position, date and time.
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.
Automatically identify suspicious or dangerous objects placed in public places.
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).
Perform facial analysis to detect attributes such as gender, age, emotion and more.
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.
Use deep face recognition to match human faces against a database in real-time videos.
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.
Read more about other use cases in different industries or learn about the deep learning technology behind modern computer vision.