Throughout the week I have been taking pictures of parking lots as I have walked to and from school each day. However, the number of ROI from my image set is still pretty small, around 650 distinct parking spaces, and this may be adversely affecting my training efforts. Most research papers that I've read have said that good results are often achieved with somewhere between 1000 and 2000 bits of training data.
The next thing that I did was to implement a cross-validation script. I ended up coding a K-fold in python which starts by randomizing the input data and then performs the cross validation. With K=5, the svm is classifying within a range of 59%-79% positive detection rate. This extremely wide range might be the result of poor randomization of the data on the part of the script and/or it might be due to the fact that I have very few night time images as part of my test data. Right now I'm going to increase the size of my test set and see if that has an effect in reducing the range of results returned by the cross-validation script.
The last thing that I worked on was to create a K-nearest neighbor classification program. I am still trying to debug the program but I hope to have it done sometime tonight or tomorrow.
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