Okay, guys, let’s dive into this project I tackled recently – building a high-accuracy people counter, specifically for public transport. It was a fun ride, and I’m here to spill the beans on how I did it.
Getting Started: The Idea
So, the whole thing started when I was stuck on a packed bus, thinking, “There’s gotta be a better way to manage crowds.” I mean, knowing how many people are on board in real-time could seriously improve things, right? More efficient routes, better scheduling, and maybe even fewer sardine-can experiences. That’s where the seed was planted.
The First Steps: Research and Choosing Tools
First things first, I hit the books (well, Google, mostly). I needed to figure out the best way to actually count people accurately. I looked at a bunch of options: pressure sensors, thermal cameras, those laser break-beam things… It was a bit overwhelming!
I finally settled on using a combination of a camera and some computer vision magic. The camera would capture the footage, and then the computer vision algorithms would do the heavy lifting of identifying and counting people.
For the hardware, I went with a pretty standard setup:
- A decent quality camera: I needed something that could handle different lighting conditions.
- A small computer: I used a Raspberry Pi because it’s compact, affordable, and powerful enough for this task.
- Some way to connect it all: Cables, power supply, the usual suspects.
Diving into the Code: The Computer Vision Part
This is where things got interesting. I’m no coding wizard, but I’ve dabbled a bit. I decided to use OpenCV, which is this awesome open-source library for computer vision. It’s got tons of pre-built functions that make things a lot easier.
The basic idea was this:
- Grab the video feed: Get the camera data streaming into the Raspberry Pi.
- Detect people: Use a pre-trained model (basically, a set of rules) that’s good at spotting people in images. OpenCV has some readily available ones.
- Draw Boxes: The model would locate the people, the I should draw a box around each person.
- Count them up: Keep track of how many people are detected in each frame.
- Handle overlaps: Make sure I’m not double-counting people if they’re close together or partially hidden. This was tricky!
It involved a lot of trial and error, tweaking parameters, and staring at lines of code until my eyes blurred. I spent hours just trying to get the detection accuracy right. There were times I wanted to throw the whole thing out the window!
Testing and Refining: Making it Work in the Real World
Once I had a basic system working, I needed to test it. I started in my living room, using myself and my family as guinea pigs. It was… rough. The lighting was a big issue. Sometimes it would count the cat as a person, which was pretty funny, but not exactly accurate.
I had to adjust the code to handle different lighting, shadows, and people wearing hats or carrying bags (which would sometimes confuse the system). I also played around with different camera angles to find the best viewpoint.
The “overlap” problem was a real pain. I ended up using some clever algorithms to track people’s movements between frames. That way, even if someone was partially blocked for a moment, the system wouldn’t lose count.
The Result: Pretty Darn Good Accuracy!
After a lot of tweaking and testing, I finally got it to a point where I was pretty happy with the accuracy. It wasn’t perfect, of course, but it was consistently getting within a few percentage points of the actual number of people in the frame. I’d say it was at least 95% accurate, which is good enough for my initial goals.
The setup is still pretty basic – a Raspberry Pi and a camera – but it proves the concept. It’s something that could definitely be scaled up and improved with better hardware and more sophisticated algorithms.
So, that’s my journey into the world of people counting! It was a challenging but rewarding project, and I learned a ton along the way. Hope this helps give any aspiring DIY people-counters out there a head start!