Feature Vocabulary

In the previous post I commented that the interest point detection algorithm I created looked like it could detect vehicles all by itself. I created a simplistic feature point counting program which 'detected' a parking spot as empty if the number of interest points in the ROI was below an arbitrary threshold. The results were moderately good but nowhere near as good as I had expected.

Threshold (min # of interest pts):
1 __ %81
2 __ %82
3 __ %77
4 __ %74
5 __ %70

The next thing I focused on was the generation of a code book of car features--small image segments centered at each interest point. For each ROI, I scale the image so as to normalize the 'zoom' of each image feature. To save a little time and memory, I only keep one feature image for interest points which are within a 3 pixel range. I then save all feature images who's center lie within the current parking space ROI being examined, positive features to one set negative features to another. Here's an example of the resulting feature vocabulary from one occupied parking space: