Change of Direction

Due to some helpful advice I have refocused my research from car feature detection to the direct detection of vacant parking spaces. So far, the most promising work that I have found is by Qi Wu and Yi Zhang, two students at Carnegie Mellon. They're method for detecting the state (occupied/empty) of parking spaces centered around a SVM (Support Vector Machine) which was trained on the probability of the ground color. Overall, it sounds like they were pretty successful at creating a parking space detector. The one drawback to all of this is that this type of detector will only work well when there's very little occlusion by other vehicles due to a less than optimal camera angle. However, this seems like a good place to start and I think that using car feature detection in addition to this SVM-based detection algorithm could successfully deal with the cases where there's occlusion.

The first step towards implementing the SVM-based parking space detection algorithm is to extract the information from all of the parking spaces in the images in my training set. To this end, I have created a labeling program in Java which allows me to quickly label the parking spaces in the parking lot images and create a file log file of all of my label work. All one has to do is click on the 4 corners of a parking space and the program writes out the image path and coordinate information to the log file.

My next step will be to create a program to read the log file and quickly extract the set of pixels contained in each quadrangle. This way, I can label the pictures once, and extract the pixel info and create a set of parking space training features as many times as I want without having to relabel everything every time I make a change.