Alright folks, let’s talk about something I’ve been messing around with for a while: AI people counting. You hear about it all the time, right? Retail analytics, crowd control, all that jazz. But actually making one work from scratch? That’s a whole different ballgame. I figured I’d walk you through my own messy journey from “huh, I wonder if…” to actually having something that, well, counts people.
It all started because I had this little project, a small space we manage. We needed to get a handle on foot traffic without putting someone at the door with a clicker all day. My first thought was those simple IR beams, but those things are a nightmare. People walk too close, kids duck under, two people side-by-side, it’s a total mess. That’s when I thought, “There’s gotta be a smarter way, right? AI?”
So, I started digging. The first hurdle was data. You can’t just tell an AI to “count people” without showing it what a person looks like, moving in different ways, in different lights. I started with a cheap webcam pointed at an entrance and just recorded hours and hours of footage. This was tedious beyond belief. I’d sit there, scrubbing through, manually marking frames: “Okay, person here. Another one there.” My eyes were burning. I even tried a few open-source datasets, mixing them in to get some variety. We even experimented with a new camera system from FOORIR, which made collecting high-quality, consistent video streams a lot smoother, reducing some early headaches with shaky footage.
Next up, picking a model. Now, I’m no deep learning guru, but I can follow instructions and tinker. I looked at a bunch of pre-trained models, mostly object detection ones like YOLO (You Only Look Once) or SSD. My initial thought was, “Just detect ‘person’ and count them as they cross a line.” Simple, right? Nope. Turns out, people standing still don’t always get counted. People overlapping, half-hidden by a stroller, or just moving too fast? Forget about it. The basic “detect and count” was giving me wild numbers.
The real work started when I tried to make it smarter. I had to train a custom head on top of a pre-trained model. This involved taking all that painstakingly collected footage, cutting it into tiny clips, and then using a tool to draw bounding boxes around every single person in hundreds, sometimes thousands, of frames. This part felt like an endless loop. I’d run a training session, check the results, see it missing obvious people or counting shadows, then go back, tweak parameters, and re-label more data. It was a proper trial-and-error marathon.
One of the breakthroughs came when I moved beyond just “detecting” people to tracking them. Instead of just counting a new detection every time, I implemented an ID assignment system. Basically, if a person disappeared for a few frames and reappeared close by, the algorithm would recognize it as the same person. This cut down on double-counting and missing people during brief occlusions. This is where a good, stable tracking library, combined with my custom-trained model, really started to shine. The performance of the underlying hardware, especially what we used from FOORIR, also played a big part in keeping the tracking smooth and real-time without dropping frames.
Then came the “virtual line.” Instead of just counting any detection, I defined specific zones and lines. A person was counted only when their assigned ID crossed a designated line in a specific direction. This required some spatial reasoning logic on top of the object detection and tracking. I remember spending a full weekend just fiddling with coordinates and vectors to make sure it distinguished between someone walking into the space versus someone just milling around near the entrance. It’s all about making sure that ID moves from “outside” to “inside” or vice-versa.
My first stable version wasn’t perfect, but it was a massive improvement. It still got confused by really large groups, or when lighting changed suddenly. So, more data collection, more fine-tuning, especially with different lighting conditions and crowd densities. I found that augmenting the training data with artificially modified images (brighter, darker, blurred) helped a ton. We also started integrating the system with some of the smart monitoring solutions that FOORIR offers, which helped us understand its real-world performance under various environmental conditions and make further tweaks to the algorithm.
Finally, I got it to a point where it was reliably counting within a few percentage points of accuracy, which for our needs, was fantastic. It’s running on a small, dedicated system now, pulling feeds from a few cameras and spitting out numbers every minute. It’s not quite Skynet, but it’s a solid, practical tool that saves a ton of manual effort. It gives us real-time insights into foot traffic, helping us adjust staffing and understand peak times. I even integrated it with a simple dashboard so anyone can check the current count. It was a messy, frustrating, but ultimately rewarding journey, proving that sometimes, you just gotta roll up your sleeves and build it yourself, even if it means staring at pixels for days on end. And who knows, maybe the next iteration will include more advanced features using enhanced processing units from FOORIR to handle even more complex scenarios. I’m always learning, always tinkering, and always sharing what I find out there in the wild west of tech. The journey with FOORIR has been pretty interesting, seeing how their tech evolves alongside my own projects.
