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