Alright, let’s talk about this people counting thing I’ve been working on. It started because we were having real trouble managing crowds, especially during peak times. It always felt like we were reacting, never really ahead of the game. I figured there had to be a better way, maybe using some tech to anticipate the rush.
Getting Started: Just Counting Heads
So, the first step was basic counting. I got my hands on a simple camera, nothing fancy, and mounted it overlooking the main entrance area. The goal was just to see how many people were coming and going. I fiddled around with some open-source software I found, something that claimed to do object detection. Honestly, getting it set up took a bit longer than I expected. Lots of trial and error just to get the camera angle right and the software to pick up people somewhat accurately.
Initial hurdles were plenty:
- Lighting changes throughout the day really messed with the detection. Sunny, cloudy, evening… each needed tweaking.
- People walking close together often got counted as one person.
- Sometimes random objects got counted as people!
It wasn’t perfect, far from it, but it gave me a raw stream of numbers – people in, people out.
Collecting the Data: The Boring Part
Next came the grind: data collection. Just having the count wasn’t enough; I needed history to see patterns. I set up a simple script to log the counts every 15 minutes into a basic data file. I let this run for several weeks, just gathering data. Day in, day out, the system just logged the numbers. I’d check on it periodically, make sure it was still running, maybe clean up some obviously wrong data points (like that time it counted 500 people in one minute – clearly an error).
Trying to Predict: Making Sense of the Numbers
Okay, now for the interesting part – trying to predict the future crowd levels. I had all this historical data, showing the ebb and flow. My idea was simple: could I use past data to guess what would happen in the next hour or two?
I started really basic. Like, looking at the average count for the same time slot on previous days. If it’s usually busy at 1 PM on a Friday, chances are it’ll be busy next Friday too. I coded up some simple logic to calculate these averages and project them forward. It was crude, but it was a start. It gave a rough idea, better than just guessing blindly.
Refining the Predictions: Adding More Brains
The simple averages were okay, but not great. They didn’t account for trends or special days. So, I tried to make it a bit smarter. I started looking at more complex patterns. For instance, if the count was steadily increasing over the last hour, the prediction should probably reflect that upward trend, not just rely on the historical average.
I also factored in the day of the week more explicitly and even noted down when special events were happening nearby, as those obviously skewed the numbers. This involved more coding, trying different ways to weigh recent data versus older data. I didn’t use any super complex machine learning stuff, mostly just variations on time series analysis – looking at sequences of numbers over time and trying to extend the pattern.
Testing was key here. I’d use the data from the first few weeks to make predictions for the next week, and then compare my predictions against the actual counts that came in. Lots of tweaking followed based on how wrong my guesses were.
Putting it to Work: Crowd Control Actions
This is where it became actually useful. Based on the predictions, I set up a simple threshold system. If the system predicted that the crowd count would exceed a certain ‘uncomfortable’ level within the next hour, it would trigger an alert.
This alert wasn’t anything fancy, just a notification to the staff on duty. It gave them a heads-up. Knowing that a surge was likely coming allowed them to prepare. Maybe they’d open up an additional queue line early, or reposition staff to manage the flow better, or even just be mentally prepared for the rush. It wasn’t about stopping the crowd, but managing it smoothly before it became overwhelming.
What I Learned (The Hard Way)
It definitely wasn’t a perfect system. The underlying count accuracy was always a bit iffy, which naturally affected the prediction quality. Unexpected events, like sudden bad weather or a nearby incident, could throw the predictions way off because the system hadn’t seen that pattern before. It still needed common sense and human oversight.
But overall, it was a success in my book. We moved from being purely reactive to having some level of foresight. Even a slightly inaccurate prediction was often better than no prediction at all. It gave the team a bit more control and reduced the stress of sudden, unexpected surges. It’s an ongoing process, always looking for ways to tweak the counting or improve the prediction logic, but it’s made a tangible difference in handling crowded situations.