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Integral Channel Features

We study the performance of ‘integral channel features’ for image classification tasks, 
focusing in particular on pedestrian detection. The general idea behind integral channel features is that multiple registered image channels are computed using linear and 
non-linear transformations of the input image, and then features such as local sums, histograms, and Haar features and their various generalizations are efficiently computed 
using integral images. Such features have been used in recent literature for a variety of 
tasks – indeed, variations appear to have been invented independently multiple times. 
Although integral channel features have proven effective, little effort has been devoted to 
analyzing or optimizing the features themselves. In this work we present a unified view 
of the relevant work in this area and perform a detailed experimental evaluation. We 
demonstrate that when designed properly, integral channel features not only outperform 
other features including histogram of oriented gradient (HOG), they also (1) naturally 
integrate heterogeneous sources of information, (2) have few parameters and are insensitive to exact parameter settings, (3) allow for more accurate spatial localization during 
detection, and (4) result in fast detectors when coupled with cascade classifiers.