Security Camera System

This project sets up a security camera system using a Raspberry Pi, AWS S3, and OpenCV for body detection. The system captures video when a body is detected, stores the video locally or uploads it to an S3 bucket, sends a notification via Pushover, and deletes the local video file after uploading.

This was made as part of the course "SSS3000R-1" at USN.

Features

• Real-time body detection using OpenCV's pre-trained Haar cascade model.
• Video recording with H264 encoding.
• Option to store videos locally or upload them to AWS S3.
• Automatic deletion of local files after successful upload to S3.
• Sending notifications with video links using Pushover.
• Logging with Loguru for debugging and monitoring.

Prerequisites

• Raspberry Pi with a camera module.
• Python 3.6 or later.
• AWS S3 account and relevant credentials.
• Haar cascade model file for body detection.
• Pushover account and API token.

Demo

Link to demo: Security Camera System Demo

Setup

1. Install Dependencies

   pip install -r requirements.txt

2. Environment Variables

   Create a .env file in the project directory and add the following environment variables:

   R2_ENDPOINT_URL=<your_s3_endpoint_url>
   R2_ACCESS_KEY=<your_aws_access_key>
   R2_SECRET_ACCESS_KEY=<your_aws_secret_access_key>
   R2_PUBLIC_URL=<your_public_bucket_url>
   PUSHOVER_APP_TOKEN=<your_pushover_app_token>
   PUSHOVER_USER_KEY=<your_pushover_user_key>

3. Configure AWS S3

   Ensure you have an S3 bucket named detection or modify the code to suit your bucket name.

4. Run the script

   python main.py [--local]

   Use the --local flag to store videos locally without uploading to S3.

Usage

• The system initializes the camera and waits for body detection.
• When a body is detected, video recording starts and continues until no bodies are detected. Additionally, it has a 7-second buffer before stopping the recording in case the body moves out of the frame.
• If the --local flag is not set, recorded videos are uploaded to the specified S3 bucket and deleted locally after a successful upload.
• After uploading the video to S3, a notification is sent using the Pushover API with a link to the video.

File Description

• main.py: The main script that sets up the camera, detects bodies, and handles video recording, uploading, and notifications.
• .env: Environment file storing AWS credentials, Pushover credentials, and endpoint URLs.

Acknowledgements

• OpenCV for providing the Haar cascade model for body detection.
• Loguru for advanced logging.
• AWS for providing scalable storage solutions.
• Pushover for providing notification services.