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Person Detection on Raspberry Pi 4 Model B

This project implements real-time person detection on a Raspberry Pi 4 Model B using YOLOv5 Nano and the Raspberry Pi 5MP Camera Module. It processes live video feeds and identifies persons in the frame using lightweight object detection.

Features

  • Real-time object detection using YOLOv5 Nano.
  • Filters detections to display only persons (class 0).
  • Saves detected frames with bounding boxes and confidence scores.

Hardware Requirements

  • Raspberry Pi 4 Model B
  • Raspberry Pi 5MP Camera Module (or compatible camera)
  • Internet connection for downloading YOLOv5 model

Software Requirements

  • Python 3.7+
  • Pipenv or Virtualenv (optional for managing dependencies)
  • YOLOv5
  • libcamera

Required Python Libraries

Make sure the following Python libraries are installed:

  • numpy
  • opencv-python
  • torch

You can install them using the following command:

pip install numpy opencv-python torch