Note: Today’s tutorial is actually a chapter from my new book, Raspberry Pi for Computer Vision. Let’s take a ride of our own and learn how to estimate vehicle speed using a Raspberry Pi and Intel Movidius NCS. I sincerely hope it will make a difference in your neighborhood. Once in the cloud, you can provide the shareable link to anyone you choose. Estimates the speed of a vehicle and stores the evidence in the cloud (specifically in a Dropbox folder).Detects vehicles in video using a MobileNet SSD and Intel Movidius Neural Compute Stick (NCS).In this tutorial, we’ll build an OpenCV project that: In most cases, the answer is unfortunately “no” - we have to look out for ourselves and our families by being careful as we walk in the neighborhoods we live in.īut what if we could catch these reckless neighborhood miscreants in action and provide video evidence of the vehicle, speed, and time of day to local authorities? When there is a speed bump, they speed up almost as if they are trying to catch some air! These drivers disregard speed limits, crosswalk areas, school zones, and “children at play” signs altogether. Many of us live in apartment complexes or housing neighborhoods where ignorant drivers disregard safety and zoom by, going way too fast. This tutorial is inspired by PyImageSearch readers who have emailed me asking for speed estimation computer vision solutions.Īs pedestrians taking the dog for a walk, escorting our kids to school, or marching to our workplace in the morning, we’ve all experienced unsafe, fast-moving vehicles operated by inattentive drivers that nearly mow us down. In this tutorial, you will learn how to use OpenCV and Deep Learning to detect vehicles in video streams, track them, and apply speed estimation to detect the MPH/KPH of the moving vehicle.
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