How to Build a DIY IoT Home Security System with Raspberry Pi That Nobody Else Knows About
I've always been fascinated by technology and finding new ways to use it to make my life easier and more secure. That's why I decided to build my own DIY IoT home security system using a Raspberry Pi. Here's how I did it and some tips so you can build your own too.
Selecting the Right Raspberry Pi Model
The Raspberry Pi is a small, affordable computer that has tons of features perfect for building a home security system. There are several models to choose from. I went with the Raspberry Pi 4 B because it has the most processing power and memory to handle recording video footage and running machine learning algorithms. The Raspberry Pi Zero models are smaller and cheaper, but less powerful.
The Raspberry Pi 4 B has a 1.5 GHz quad-core processor, 4 GB of RAM, fast Gigabit Ethernet, dual-band WiFi, and Bluetooth 5.0. This gives it plenty of horsepower for a complex home security system while still being energy efficient.
Installing the Operating System
The Raspberry Pi doesn't come with an operating system installed. You need to install one yourself. I chose Raspberry Pi OS since it's optimized for the Pi hardware.
To install it, I downloaded the Raspberry Pi Imager app on my laptop. Then I inserted a microSD card into my laptop, opened the Imager app, selected Raspberry Pi OS, and clicked "Write". This installed Raspberry Pi OS onto the microSD card.
Once finished, I inserted the microSD card into the Raspberry Pi and powered it on. Raspberry Pi OS booted up automatically.
Setting Up the Camera Module
To capture video footage, I needed to add a camera module. The Raspberry Pi Camera Module V2 is made specifically for the Pi and connects right to the board.
I attached the camera ribbon cable to the CSI port on the Raspberry Pi, installed the camera software with the command sudo apt-get install raspberrypi-v2-camera-libs, enabled the camera in Raspberry Pi Config, and rebooted.
To test it, I opened a terminal and entered raspistill -o test.jpg to take a photo. It worked perfectly!
Adding Motion Sensor Components
To detect motion and trigger recording, I wired up some HC-SR501 PIR motion sensors. These detect infrared radiation from heat signatures up to 20 feet away.
I connected the sensor's VCC pin to the Raspberry Pi's 5V GPIO pin, the GND pin to ground, and the OUT pin to GPIO 4. I enabled GPIO 4 as an input in the Pi's settings. Now the Pi can detect a voltage signal when the sensor triggers.
Writing a Python Script to Capture Video
Here's where the magic happens. I wrote a Python script to start recording video when the sensor pin goes high.
First I imported the necessary modules like GPIO and picamera. In an infinite loop, I check the GPIO 4 pin. If motion is detected, I create a Camera object and record 10 seconds of 720p video to a file named with the timestamp.
This gives me short video clips automatically captured whenever the sensor detects movement!
Live Streaming the Camera Feed
Having recordings is great, but I also wanted to be able to check the live camera feed. The Pi makes this easy with streaming over RTSP.
I installed the libcamera dependencies with sudo apt install libcamera-apps. Then when I want to stream, I run libcamera-vid -t 0 --width 1280 --height 720 -o tcp://0.0.0.0:8000.
This streams the live video feed from the Pi camera over my local network. I simply enter the stream URL into VLC media player on my laptop to view the live footage!
Automating Processing with Docker and TensorFlow
As a final step, I wanted to automate analyzing the camera footage with machine learning. This involved setting up a Docker container running TensorFlow on the Raspberry Pi.
First I installed Docker CE on Raspberry Pi OS. Then I pulled the tensorflow/tensorflow Docker image and ran it with the camera device mounted.
In the container, I wrote a Python script that loads a trained model and analyzes each video frame for detected objects, sending alerts if anything suspicious is found.
The Docker container runs this in the background, automatically processing footage!
Conclusion
Building my own DIY home security system with Raspberry Pi was an extremely fun and rewarding project. I ended up with a fully-featured system capable of motion detection, video recording, live streaming, and automated analysis with TensorFlow.
The Pi made it affordable and easy to get everything up and running. Now I have peace of mind knowing my home is more secure, and I learned a ton about IoT and machine learning in the process! Let me know if you have any other tips for enhancing a Pi home security system.