The final approach I took in order to detect motion was to do Background substraction and tune the algorithm present on the openCV website, in order to filter out noise but also to make more resilient.I convert the frame into grayscale, then apply the Background Substraction algorithm. The result of this should be only black & white values, but due to heavy light conditions ( where present ), we also encounter median values. Therefore, I also apply a binary threshold algorithm to the result, in order to filter out these values. I then use the openCV implementation findCountours in order to find all the countours in the image. I calculate the area of all of them, and filter the low-area ones. Finally, for viewing purposes, I draw the result.
In the next blog post, I will be talking about how we will be handling communication between the camera and the other components of our device.
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