Wildlife Camera Trap Kit v2 — AI Student Engagement Monitor with Raspberry Pi 5 and MediaPipe

Every part needed, pre-tested for compatibility, with an AI build companion trained on this exact project. Shipped from Bengaluru in 3-5 days.

Bring ethical AI into the classroom. This kit lets you assemble a privacy-first device that classifies student engagement using head pose and posture — all processed locally on a Raspberry Pi 5. No cloud, no personal data stored, just anonymous aggregate analytics ready for teacher dashboards.

What You're Building

You’ll create a compact, wall‑mountable vision unit that monitors a classroom in real time. Using MediaPipe’s face‑mesh and pose‑landmark pipelines, the system detects attentiveness, drowsiness, and off‑task behavior purely from head orientation and upper‑body stance. Reports are stored on the NVMe SSD for weekly review, helping educators understand class energy without ever identifying an individual student.

What You’ll Learn

• Configure a Raspberry Pi 5 with NVMe SSD and M.2 HAT for fast model inference and log storage
• Calibrate the Pi Camera Module 3 Wide to capture the full classroom field of view
• Run MediaPipe face and pose detection pipelines on‑device using Python
• Design a head‑pose and posture‑based engagement classifier with adjustable thresholds
• Generate anonymous CSV reports with session‑wise attention percentages

Kit Contents

Why Buy This Kit Instead of Sourcing Parts Separately

Who This Kit Is For

CBSE Class 11–12 students taking the AI elective, B.Tech CSE/ECE undergraduates at IITs, NITs, VIT, and BITS, Smart India Hackathon teams prototyping human‑behavior analytics, and ATL Tinkering Labs exploring real‑world computer vision. If you have basic Python skills and curiosity about on‑device AI, this kit turns a weekend into a working EdTech prototype.

Built and Backed by Compoden

Every Compoden kit ships with an AI build companion trained on this exact project — accessible