Interest Point Detection

So the next stage of the project is to bring in vehicle feature detection to make the overall algorithm more robust. To this end, I read Agarwal and Roth's paper on "Learning a Sparse Representation for Object Detection" and it seemed like an excellent place to start.

The first thing I needed to do was implement interest point detection and test it out on some of my training data. Kristen was kind enough to give me a copy of her implementation of Förstner Corner detection. But like Kristen warned me, Förstner worked fine on the test image of a checkers board but it didn't pick much up in my training images (see below):


I then used OpenCV's Harris Corner detection and found it to be extremely good for my project (see below):


The interesting thing about this Harris Corner detection algorithm is that even without creating a database of vehicle features and a database of non-vehicle features and then using that to detect vehicles, the interest points themselves are actually very accurate at determining where a vehicle is. However, I'm still going to try and reproduce as much of Adarwal and Roth's research, time permitting ;-)