Alright, let’s talk about this people counting system I put together. It wasn’t some fancy, off-the-shelf thing. I needed something specific, something that could talk to our existing business software, so I decided to just build it myself.
Getting Started – The Hardware Bit
First things first, I needed eyes. I looked at different options, like fancy thermal sensors, but ended up going with some decent IP cameras. Cheaper, and I figured I could handle the video processing. I already had a few lying around from another project, so that saved some cash. I mounted one camera above the main entrance, angled just right to get a clear view of people coming in and out.
Setting it up wasn’t too bad:
- Plugged the camera into our network.
- Configured its IP address so I could access the video stream.
- Made sure the view covered the whole doorway without too many blind spots.
That was the easy part, really. Just getting the hardware in place.
Making it Count – The Software Challenge
Next up, actually counting people. This took some fiddling. I didn’t want to reinvent the wheel entirely, so I looked into existing computer vision libraries. OpenCV came up a lot, so I decided to dive into that. I set up a basic script on a small server we had.
The idea was simple: grab frames from the camera stream, detect people, and track them as they crossed a virtual line I defined in the camera’s view. One line for ‘in’, another for ‘out’.
Honestly, it took a while to get right:
- Lighting changes messed things up constantly. Morning sun vs. cloudy afternoons made detection tricky.
- Sometimes it counted shadows, or people standing too close together got missed or counted as one.
- Lots of tweaking parameters, adjusting the detection sensitivity, trying different background subtraction methods. It was messy.
Eventually, I got it working reliably enough. Not perfect, but good enough to give us a decent idea of foot traffic.
The Real Goal: Talking to Our Business Software
Just having a counter wasn’t the end game. The whole point was to get this data into our other systems. We use a pretty standard CRM and some custom reporting tools. I wanted to see visitor counts alongside sales data, or staff scheduling, without manually punching numbers in.
This meant figuring out how to make my little counting script talk to these other pieces of software. Our CRM had an API, which was helpful. The reporting tool was a bit more locked down, but I found it could import data from a specific database table.
Hooking Everything Up
So, the plan became: my counting script would log counts (ins and outs with timestamps) to a simple database. Then, another small script would run periodically:
- Read the latest counts from my logging database.
- Format this data correctly for the CRM’s API.
- Make API calls to push the hourly visitor count into a custom field in the CRM.
- Insert the same data into that specific table the reporting tool could read.
Getting the API calls right took some back and forth. Authentication was the first hurdle, then making sure the data format matched exactly what the CRM expected. Debugging felt like talking to a brick wall sometimes – send a request, get an error, tweak the request, try again.
For the reporting tool integration, setting up the database connection was straightforward. Just had to make sure the table structure was correct and my script had the right permissions to write to it.
Testing and Living With It
Once I thought it was working, I let it run for a few days, constantly checking. I compared the system’s counts against manual counts during busy periods. It wasn’t 100% accurate, maybe off by 5-10% sometimes, but it was consistent. The key was that the trend was accurate, and the data was flowing automatically into the CRM and reporting system.
Now, we can pull up reports showing foot traffic patterns alongside sales conversion rates for the day or week. We can see if staffing levels match busy periods. It’s not revolutionary, but it connects the physical world (people walking in) to our digital business data in a way we couldn’t before without tons of manual work.
It was definitely a learning process, lots of trial and error, especially getting the different systems to talk nicely. But having that integrated data flow now makes it totally worth the effort.