My New Year's resolution is to give my car a rudimentary visual system, which should give me plenty to blog about.
My initial goal is to train the car to identify police cars, mount 4 cameras to allow for panoramic view, and have some kind of audible or visual warning. Is identifying a police car feasible? I'm not a computer vision expert, but searching around these points appear salient:
- single object class: the question is binary, "does this picture contain a police car?", which makes the problem easier.
- pose variability is low: the cameras will be mounted at fixed points at my car, and both my car and any police car will (barring the "Dukes of Hazzard" scenario) have all four wheels on the ground, which should moderate the pose variability.
- volatile illumination: ideally, the detector would operate under all weather conditions, day or night. so that means significant illumination changes.
Well, we see how far I get anyway.
Step #1 was to get a brain for my car, so I purchased a refurb Dell Mini 110-1030nr for $280 from Amazon and put Ubuntu Netbook Remix on it. I also got 1 webcam for now.
It is presumably underpowered for the image processing that will be required, but if I get that far that will justify spending more money. First, I need to install it in the car and have it do video capture; from the resulting data I imagine many interesting problems will suggest themselves.