Okay, so I wanted to share something I’ve been tinkering with lately. It’s about figuring out how crowded a space gets, you know, for managing things better. Maybe it’s a shop floor, a waiting area, or even just a part of an office.
Getting Started: Why Bother?
It all started because we had this common area that sometimes got ridiculously packed, and other times it was a ghost town. We were trying to figure out staffing or maybe even changing the layout, but it was all guesswork. Someone would say “It felt busy this morning,” but what does that even mean? I thought, there must be a better way than just eyeballing it.
The First Idea: Cameras!
My first thought was pretty simple: use a camera. We already had a couple of basic security cameras around, nothing fancy. I figured, maybe I could tap into that feed. The goal wasn’t to watch people, specifically, but just to get a sense of how many people were in a certain zone at any given time.
Trying Things Out: The Setup
So, I grabbed an old webcam I had lying around first, just to test the waters in a smaller, controlled space – my workshop, actually. I hooked it up to a spare computer. Getting the camera angle right was the first little headache. Too low, and people blocked each other. Too high, and it felt weirdly invasive, even though it was just for counting.
I settled on a fairly high angle, looking down, covering the main area I wanted to monitor. This seemed to give the best view without being too ‘in your face’.
Making Sense of the Video: The Software Bit
Now, just having a video feed doesn’t tell you the density. I needed software. I’m not a super coder, mind you, but I like to mess around. I looked into some existing tools, but they seemed complicated or expensive. So, I decided to try piecing something together myself using some open-source stuff I found online. Basically, the idea was to get the computer to look at the video frame by frame and try to ‘spot’ things that look like people.
This part took ages. Seriously, lots of trial and error.
- First attempts were terrible. It counted shadows, chairs, bags left on the floor… you name it.
- Lighting was a big issue. Bright sunlight coming through a window would mess everything up. Evenings, when it got darker, also caused problems.
- People standing too close together? The system often counted them as one big blob.
I spent quite a bit of time tweaking the settings in the software I was using. Fiddling with sensitivity, trying different methods to detect shapes. I even tried a simpler approach for a bit – instead of counting heads, just looking at how much of the ‘motion’ was happening in the frame. That was okay-ish, but not really giving me a count.
Getting Somewhere: Refining the Count
Eventually, I landed on a setup that was sort of working. It wasn’t perfect, not by a long shot, but it could give a rough estimate. I wasn’t aiming for pinpoint accuracy, just a general trend. Is it ’empty’, ‘a few people’, ‘getting busy’, or ‘packed’?
I added a simple step: define the specific zone on the camera view I cared about. Ignore everything outside that box. Then, the software would try its best to count the ‘people shapes’ inside that zone. I even programmed it to average the count over, say, a minute, to smooth out quick movements.
Putting It to Use: Did it Help?
So, I let this run for a few weeks, collecting data. Just simple numbers logged every five minutes. And you know what? It actually started showing patterns. We could clearly see the peak times, the lunch rush, the quiet afternoons.
It wasn’t about watching individuals; it was about the flow. Based on this, we could make some real decisions:
- Adjusting staff breaks so more people were available during actual busy times, not just ‘guessed’ busy times.
- Thinking about maybe adding more seating because the ‘packed’ times were really uncomfortable according to the numbers.
- We even noticed a bottleneck near the entrance just by seeing where the density consistently got high.
Privacy was important too. I made sure the system wasn’t recording footage long-term, just processing it live for the count. And the resolution was kept low enough that you couldn’t really recognize faces anyway. It was just about the numbers.
Final Thoughts
Look, it wasn’t a professional, off-the-shelf system. It had its quirks. Sometimes it would undercount or overcount. But for a practical tool built with fairly basic parts and some persistence, it did the job surprisingly well. It gave us actual data to work with instead of just gut feelings. It took time and fiddling, definitely wasn’t a plug-and-play thing, but seeing those patterns emerge from the raw counts was pretty rewarding. It definitely helped us manage that space a bit more intelligently.