Okay, so here’s the deal. I’ve been messing around with people counting for a while now, mostly because I thought it could be super useful for emergency situations, like, you know, fires or earthquakes. The basic idea is to figure out how many people are in a building and how they’re moving so we can plan better evacuations and make things safer.
First things first, I started by looking at different ways to actually count people. I played around with a few options, like using security camera footage and trying to detect people with some basic image recognition stuff. That was a pain. The lighting always sucked, and the quality was usually potato-level. Plus, people bunch up and it becomes a blob. Not ideal.
Then I stumbled upon some research about using thermal cameras. The idea being that heat signatures are easier to pick up and aren’t as affected by bad lighting. So, I managed to snag a cheap thermal camera online. It wasn’t anything fancy, but it was enough to get started.
Next up was the software. I decided to use Python because, well, everyone uses Python, right? I used OpenCV for image processing to read the thermal camera feed. This was a bit of a headache because the thermal images are different from regular images. You get temperature values instead of colors, so I had to mess around with scaling and color mapping to visualize things properly.
After I had a decent visual feed, I started trying to detect people. I tried a few different methods. Initially, I tried some basic thresholding. Basically, I set a temperature threshold, and anything above that temperature was considered a person. Surprisingly, it worked… kinda. But it was also picking up hot pipes and sunlight coming through windows. Back to the drawing board.
I then started looking into more advanced object detection methods, like using pre-trained models for human detection. I experimented with YOLO and SSD, but they weren’t really designed for thermal images, so the accuracy wasn’t great without a lot of tweaking. So I tried a different approach using background subtraction.
The idea behind background subtraction is simple: you create a model of the background (empty room) and then subtract it from the current frame. Anything that’s left over is considered a foreground object (hopefully, a person). I used Gaussian Mixture Models (GMM) from OpenCV for the background subtraction. It took some fine-tuning to get the parameters right. You know, things like the learning rate and the number of components in the mixture. But once I got it dialed in, it was actually pretty good at isolating people.
Once I could detect people, I needed to track them. I used a simple centroid tracking algorithm. The basic idea is to calculate the center point (centroid) of each detected person in each frame and then match them up based on proximity. It wasn’t perfect, especially when people crossed paths, but it was good enough for my purposes.
Now, to make this whole thing useful for emergency evacuation planning, I needed to get some actual data. I set up the camera in a hallway and recorded some footage of people walking by. Then, I ran my code on the footage to count the number of people and track their movement.
I visualized the results using Matplotlib. I created plots showing the number of people in the hallway over time and heatmaps showing the most frequently used routes. You know, just basic stuff but actually super helpful to see where people tend to go during a normal day.
Here’s where things get interesting: I started thinking about how to use this data to simulate evacuation scenarios. I figured I could use the movement patterns to predict how people would move during an emergency and identify potential bottlenecks. This is where I got into agent-based modeling. Basically, each person becomes an “agent” that follows certain rules based on their observed behavior.
I used a simple rule-based system where agents would move towards the nearest exit while avoiding obstacles and other agents. I ran simulations with different numbers of people and different exit configurations. The results were pretty cool. I could see how different factors, like the width of the exits or the presence of obstacles, affected the evacuation time. It was very eye-opening!
- Figuring out thermal cameras aren’t magic bullets, but they are a decent tool.
- Simple algorithms can often do the trick, especially with some fine-tuning.
- Data visualization is key to understanding the results and communicating them to others.
This whole project was a lot of work, but it was also incredibly rewarding. I learned a ton about image processing, object detection, and simulation. I’m by no means an expert, but I feel like I have a solid understanding of the basics. And more importantly, I feel like I’ve made a small contribution to making buildings safer for everyone.
What’s next? Well, I’m planning to experiment with more advanced agent-based models and incorporate more realistic behaviors. I also want to integrate this system with existing building management systems to provide real-time evacuation guidance. But that’s a story for another time.