I really need to find the time to build this DIY speed cam. From my home office window, I have an excellent view of an intersection where I would estimate about 70% of the cars don’t even stop at the posted Stop sign. Further, I would guess that close to 90% of them are going faster than the 25 MPH speed limit. Data is good.
Computer vision itself isnâ€™t anything new, but it has only recently reached a point where itâ€™s practical for hobbyists to utilize. Part of that is because hardware has improved dramatically in recent years, but it also helps that good open-source machine learning and computer vision software has become available. More software options are becoming available, but OpenCV is one that has been around for a while now and is still one of the most popular. Over on PyImageSearch,Â Adrian Rosebrock has put together a tutorialÂ that will walk you through how to detect vehicles and then track them to estimate the speed at which theyâ€™re traveling.
Rosebrockâ€™s guide will show you how to make your very own DIY speed camera. But even if that isnâ€™t something you have a need for, the tutorial is worth following just to learn some useful computer vision techniques. You could, for instance, modify this setup to count how manyÂ carsÂ enter and exit a parking lot. This can be done with affordable and readily-available hardware, so the barrier to entry is low â€” perfect for the kind of project that is more of a learning experience than anything else.