Learn how to fork our computer vision template and deploy an application that uses London's traffic cameras to gauge current congestion by leveraging YOLOv8.
If you've ever wanted to experiment with computer vision but don’t have the time to set up a complex development environment, this tutorial is for you. Computer vision can be a great substitute for physical sensors in certain situations—especially when it comes to counting things. In this tutorial, I’ll walk you through a demo application that we’ve released that uses London's traffic cameras to gauge current congestion by counting vehicles.
Ov8. We are just using object detection, but to count vehicles properly, you’ll need to use object tracking. The problem is, object tracking requires more memory, which is not available in the Quix free plan. This demo uses the smallest “nano” Ov8x being the most powerful. If you use a larger model, you can get great results for vehicle tracking and counting. Here’s a screenshot from an attempt to run it on my local machine on a TfL camera feed. I used
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