Okay, so I’ve been messing around with this idea for a while now – an advanced people counting system, specifically for places like airports and train stations. You know, those crazy busy hubs where it’s a nightmare to figure out how many folks are where at any given time. I figured, there’s gotta be a better way to track this stuff, right?

So, I started by just watching. I went to our local airport, just sat there with a notebook, and tried to count people manually. Total disaster. People are moving too fast, in different directions, and it’s easy to miss them or count them twice. I definitely needed something a bit more, shall we say, tech-savvy.

Then, I started thinking about cameras. Everyone’s got cameras these days, right? So I grabbed a few cheap webcams I had lying around and set them up in my living room (my wife thought I was nuts). I tried to get them to just count heads. This was a mess too. The lighting was all wrong, the angles were off, and the software I found online, well, let’s just say it wasn’t exactly built for this.

After a bunch of trial and error, I started playing with some open-source computer vision libraries, like OpenCV. That was a game-changer. I could start to detect people in the video feed, but it was still super basic. They were just blobs, and the system would get confused if people were too close together or if they were carrying stuff. It was a start, but not good enough for a real airport or a busy station.

So I dove deeper into the whole machine learning thing. It was a big learning curve, but I started training the system to recognize people more accurately. I fed it tons of images of people in different positions, with different lighting, carrying bags, pushing strollers, the works. I used a bunch of free datasets I found online and even took my own pictures and videos. Man, that took a long time.

The Setup

  • Cameras: I upgraded to some higher-resolution IP cameras that could handle different lighting conditions. It was a bit of an investment, but it made a huge difference in the quality of the video feed.
  • Processing Power: I realized I needed a dedicated machine to do all the heavy lifting, so I built a small PC with a decent graphics card. Nothing too fancy, but enough to handle the real-time video processing.
  • Software: I stuck with OpenCV and started using some pre-trained models for people detection. I had to tweak them a bit to get them to work better in my specific setup, but it saved me a ton of time.

After weeks, maybe even months, of tweaking and testing, I finally had something that was starting to work. It could count people fairly accurately, even in crowded areas, and it could even track their movement to some extent. I was still getting some false positives (like counting a poster of a person as a real person), but it was way better than where I started.

The next step was to set up a simple dashboard to display the counts in real-time. I just used some basic web development stuff I know to put together a simple interface. It wasn’t pretty, but it did the job. I could see the counts from each camera, and I even added some simple graphs to show the trends over time.

I’m still working on it, of course. There’s always room for improvement. I want to make it even more accurate, maybe even figure out how to estimate things like age and gender. Who knows, maybe one day this thing will actually be useful in a real-world setting. For now, it’s just a fun project that’s keeping me busy. And hey, at least I can finally tell how many people are in my living room at any given time. Small victories, right?