Integral Channel Features

We study the performance of ‘integral channel features’ for image classification tasks,
\nfocusing in particular on pedestrian detection. The general idea behind integral channel features is that multiple registered image channels are computed using linear and
\nnon-linear transformations of the input image, and then features such as local sums, histograms, and Haar features and their various generalizations are efficiently computed
\nusing integral images. Such features have been used in recent literature for a variety of
\ntasks – indeed, variations appear to have been invented independently multiple times.
\nAlthough integral channel features have proven effective, little effort has been…