Student counter

The Student Counter system leverages a head detection model to automatically estimate the number of students present in a classroom or any monitored space using surveillance cameras. Instead of relying on manual attendance or traditional face recognition, the model focuses on detecting heads, which makes it robust even in crowded or partially occluded environments.

For this project, I have utilized the YOLOv8 Nano model, chosen for its lightweight architecture and high inference speed. This allows the system to operate in real-time, making it suitable for deployment on edge devices or live surveillance setups.

Key points:

Head detection ensures accurate counting even when faces are not fully visible.

YOLOv8 Nano provides fast detection with minimal computational resources.

️ Works seamlessly with real-time video streams from classroom or surveillance cameras.

Enables automated monitoring, attendance tracking, and space utilization analysis.