Bicycle Dashcam Part 4: New Hardware
I was reading an article about Oak Vision Modules on Hackaday, and thought, wow, this is the PERFECT platform for my bicycle dashcam. The Oak Vision module is a Kickstarter project with camera modules, depth mapping capability using stereo vision, and a processor (Intel Movidius Myriad X) designed to accelerate machine vision in 1 package for $149US - see https://www.kickstarter.com/projects/opencv/opencv-ai-kit/
At the 3:55 mark in the marketing video, I THEN see the board mounted to a bicycle saddle, which is EXACTLY what I want to do:
I went to see what I could find about the developers, and read about them on TechCrunch:
“The actual device and onboard AI were created by Luxonis, which previously created the CommuteGuardian, a sort of smart brake light for bikes that tracks objects in real time so it can warn the rider. The team couldn’t find any hardware that fit the bill so they made their own, and then collaborated with OpenCV to make the OAK series as a follow-up.”
This is pretty exciting - CommuteGuardian is the first project I’ve come across with similar goals to mine: Prevent and Deter Car-Bicycle accidents. I exchanged a few emails with Brandon Gilles, the Luxonis CEO, and he shared some background - they also checked out OpenALPR, and started work on mobile phone implementations, but decided to move to a custom board when the Myriad X processor was launched.
You can read more about CommuteGuardian here:
I decided to back Luxonis’ Oak project. I’ll have to learn some new tools, but this board will be much faster than the Pi for image analysis (much faster than the 1 frame per 8 seconds I’m getting now!). The stereo vision capabilities on the Oak-D will allow for depth mapping, a capability for which I had previously been considering adding a LIDAR sensor. Looking forward to receiving my Oak-D, hopefully in December. In the interim, I’ll continue to experiment with different license plate recognition systems, read more about the tooling I can use with Oak-D, and perhaps try a different camera module on the Pi.