Okay, so I wanted to share this project I’ve been working on – monitoring foot traffic at transportation hubs. It’s been a real learning experience, so I figured I’d walk you through what I did, step by step.
Getting Started: The Idea
I started thinking about how useful it would be to have real-time data on how many people are moving through train stations, bus terminals, and stuff like that. This info could help with all sorts of things, like managing crowds, scheduling staff, and even planning future infrastructure.
Choosing the Tools
First, I needed to figure out how to actually count people. I explored a few options:
- Manual Counting: Yeah, no. Way too labor-intensive and not very accurate.
- Turnstiles: Some places already have these, but they don’t capture everyone (think about people with luggage or strollers, or emergency exits).
- Computer Vision: This seemed like the most promising. Using cameras and some clever software to identify and track people.
I decided to go with the computer vision route. I found some open-source libraries like OpenCV and some pre-trained models that could detect people in images. I’m no expert programmer, so this was a bit of a challenge, but I found some great tutorials online.
Setting Up the Hardware
Next, I needed some hardware. I used:
- Cameras: I grabbed a couple of basic webcams to start. Nothing fancy, just something to test with.
- A Computer: I used my old laptop, which was powerful enough to run the software.
I positioned the cameras in a test area (my hallway, actually!) to get a good view of people walking by.
The Software Side
This is where things got a little tricky. Here’s what I basically did:
- Install the Libraries: I had to install OpenCV and some other dependencies on my laptop. This involved a lot of Googling and following instructions carefully.
- Load the Model: I downloaded a pre-trained model for object detection (specifically, people detection).
- Write the Code: I cobbled together some Python code that would:
- Grab the video feed from the cameras.
- Run the model on each frame to detect people.
- Draw boxes around the detected people (just to see if it was working!).
- Keep a running count of the number of people detected.
Testing and Refining
It took a lot of trial and error. At first, the software was detecting my cat as a person (which, to be fair, he acts like one sometimes). I had to tweak the settings, adjust the camera angles, and generally just mess around until it was reasonably accurate. Lighting was a big issue – too bright or too dark, and it would mess things up.
Getting the Data Out
Once I had the basic counting working, I needed a way to actually use the data. I added some code to:
- Display Real time number: Showing real time number on the screen.
The Results (So Far!)
It’s still a work in progress, but I’ve got a basic system that can count people passing through a specific area. It’s not perfect, but it’s a good starting point. It can work now!
This whole project has been a reminder that even seemingly complex problems can be tackled with a bit of persistence and some readily available tools. I’m excited to keep improving it and see where it goes!