Integral Channel Features

Abstract

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 devoted to
\nanalyzing or optimizing the features themselves. In this work we present a unified view
\nof the relevant work in this area and perform a detailed experimental evaluation. We
\ndemonstrate that when designed properly, integral channel features not only outperform
\nother features including histogram of oriented gradient (HOG), they also (1) naturally
\nintegrate heterogeneous sources of information, (2) have few parameters and are insensitive to exact parameter settings, (3) allow for more accurate spatial localization during
\ndetection, and (4) result in fast detectors when coupled with cascade classifiers.