Improving cascade of classifiers by sliding window alignment in between
Karel Zimmermann, David Hurych, Tomáš Svoboda · 2011
We improve an object detector based on cascade of classifiers by a local alignment of the sliding window. The detector needs to operate on a relatively sparse grid in order to achieve a real time performance on high-resolution images. The proposed local alignment in the middle of the cascade improves its recognition performance whilst retaining the necessary speed. We show that the moment of the alignment matters and discuss the performance in terms of false negatives and false positives. The proposed method is tested on a car detection problem.