Okay, folks, let’s dive into this project I’ve been tinkering with – a people-counting system. The idea is to track how many people are moving around in, say, a park or a shopping area. Not in a creepy, Big Brother way, but more like a general “how busy is this place?” kind of deal.
I started by grabbing a Raspberry Pi. I mean, what doesn’t start with a Raspberry Pi these days? I also got my hands on a Pi Camera – nothing fancy, just the standard one.
Next up, I had to figure out the software side of things. I’m no coding whiz, but I’ve dabbled with Python before. After some digging around, I came across OpenCV. It’s basically a library with a bunch of pre-built tools for image and video processing. Perfect!
Setting Up the Pi
- I flashed the Raspberry Pi OS onto an SD card. Pretty standard stuff.
- Hooked up the camera and made sure it was working.
- Enabled the camera interface in the Raspberry Pi configuration. You gotta do this or it won’t work.
- I also set up SSH so I could control the Pi from my laptop. No need to lug a monitor around.
- Of cource I installed OpenCV with Python.
The Code (Simplified, of Course)
The basic idea of the code is this:
- I Capture video from the camera.
- Convert the video frames to grayscale. Makes things easier to process.
- I Used some OpenCV magic to detect moving objects. This involves comparing frames and looking for differences.
- I Draw boxes around the moving objects. Just to visualize what’s happening.
- And then used the detection and draing boxes, I Count the number of moving objects (people) that cross a certain line on the screen.
It’s not perfect, of course. Sometimes it would count a dog as a person, or a fast-moving shadow. But for a rough estimate, it seemed to do the trick. I played around with the sensitivity settings to try and minimize false positives.
The Results
After some fiddling and tweaking, I managed to get a pretty decent count of people moving through a frame. I set it up on my balcony overlooking a small park, and it was surprisingly accurate, more or less.
It’s definitely a work in progress. I’d like to improve the accuracy, maybe by using a better object detection algorithm. And it would be cool to add some kind of data logging, so I could see how the numbers change over time. But for a weekend project, I’m pretty happy with how it turned out!