Create an Intelligent Classroom Solution

Provide live classroom metrics, such as class attentiveness, participation, happiness index, and attendance without class disruption.

Target Operating System Ubuntu* 16.04 LTS
Time to Complete 30 minutes

GitHub* (C++)

What You Will Learn

Learn how to build multiple computer vision inference neural networks that unobtrusively gather data on student behavior and activity in the classroom.

Use the skills learned in this reference implementation to develop similar IoT solutions. This solution uses the following neural networks:

  • Computer vision applications for IoT
  • Inference to analyze video data
  • Visual solutions market in IoT

Learn to build and run an application with these capabilities:

Detect the attentiveness of students in the classroom through head-pose estimation inference models
Capture real-time classroom activity and determine attendance through action detection and facial re-identification
Visualize the output in a data-rich dashboard

How It Works

The intelligent classroom solution demonstrates how to combine several neural networks to detect real-time classroom metrics.

  • The application marks students looking toward the front of the class as attentive. To calculate this metric, the application uses head-pose-estimation-adas-0001 to get the value of the head pose of each student and calculate the mean.
  • To capture real-time class participation metrics (such as students standing or raising a hand), it uses person-detection-action-recognition-0005.
  • To detect the average emotions of the students and calculate the overall mood of the classroom in real time, it uses the pretrained model emotions-recognition-retail-0003.
  • To calculate the total class attendance, it uses the pretrained model face-reidentification-retail-0095 to detect individual faces.

A multiplatform computer vision solution for smart cameras, video surveillance, robotics, transportation, and more.

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