Okay, so I’ve been messing around with this idea of tracking how many people are in a store at any given time. You know, for, like, optimizing things. Figured I’d share my little project, start to finish.
Getting Started
First, I needed a way to, well, see people. I grabbed a cheap webcam – nothing fancy, just something that could stream video. My thinking was, if I could get a live feed, I could probably figure out how to count the humans in it.
Then came the software part. I’m not a coding wizard, but I know my way around Python a bit. I found this library called OpenCV, which is apparently the thing for computer vision stuff. It felt a little intimidating at first, but there are tons of tutorials online.
The Messy Middle
I started by just getting the webcam feed to show up on my screen using OpenCV. That was surprisingly easy! A few lines of code and bam, live video. Feeling pretty good about myself at this point.
Next, the hard part: actually detecting people. I tried a few different things here. Initially, I fiddled with some pre-trained models that came with OpenCV. There was one for detecting “full bodies,” which seemed perfect. It… kinda worked. It would draw boxes around people, but it was also drawing boxes around chairs, posters, and basically anything vaguely human-shaped. Not exactly “real-time” accurate.
So, back to the drawing board (aka Google). I stumbled upon something called a “HOG detector” (Histogram of Oriented Gradients). Don’t ask me to explain the math, but it’s basically a smarter way to find people in images. I plugged that into my code, replacing the old “full body” detector.
- Improvement Number One: It was definitely better! Fewer false positives (meaning, it wasn’t freaking out about chairs anymore).
- Improvement Number Two: Still not perfect. It would sometimes miss people if they were partially hidden, or if the lighting was weird.
I spent a good chunk of time tweaking parameters, messing with thresholds, and generally just poking at the code until it behaved a little better. This is where the “real-time” part became a challenge. Processing each frame of video takes time, and if it takes too long, your count is going to be way off.
Getting (Somewhat) Accurate
To speed things up, I did a couple of things:
- Reduced the frame size: Smaller images are faster to process.
- Only processed every few frames: Instead of analyzing every single frame, I skipped some. This made the count a little less jumpy, but also a little less responsive.
I also added some simple logic to try and track people as they moved around. Basically, if a box appeared in roughly the same spot in consecutive frames, I assumed it was the same person. This helped prevent double-counting if someone just shifted slightly.
The “Good Enough” Result
After all that tinkering, I had something that… mostly worked. It wasn’t going to win any awards for accuracy, but it could give a reasonable estimate of how many people were in the camera’s view. I displayed the count on the screen, updating it in ( মোটামুটি) real-time.
It’s definitely a work in progress. There’s a ton of room for improvement. I could probably use a better camera, a more powerful computer, and definitely some more sophisticated algorithms. But for a quick and dirty project, I was pretty happy with it.
If anyone use this, remember my experiences, it is not a easy work, you need to have the patience! I think I will continue to make it better.