Parking Space Classification using Convoluional Neural Networks
Our overall goal is to develop an automated system to count the empty parking spaces in a parking lot, given only an image of the lot (and, optionally, the bounding boxes of the spaces) as input. Two possible approaches are explored to achieve this: a binary classifier that can label an individual space image as empty or occupied, and a multinomial classifier (a.k.a. counter net) that returns the number of empty spaces given the entire lot image. The binary classifier has yielded very encouraging results, with over a 99% overall accuracy and strong indications that it would generalize well to other parking lots. However, the major downside is that it requires the bounding boxes of the parking spaces, limiting its usefulness to stationary camera feeds. The counter net has an overall accuracy of about 81% much lower than the binary classifier, and its errors exhibit a wide variance. This is a useful baseline, but more improvements should be made to make it more practical for real world use. If this approach can be improved, however, it would be the more useful of the two since it only requires the image as input.