TY - GEN

T1 - Integral channel features

AU - Dollár, Piotr

AU - Tu, Zhuowen

AU - Perona, Pietro

AU - Belongie, Serge

PY - 2009

Y1 - 2009

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=84898842272&partnerID=8YFLogxK

U2 - 10.5244/C.23.91

DO - 10.5244/C.23.91

M3 - Conference article

AN - SCOPUS:84898842272

JO - British Machine Vision Conference, BMVC 2009 - Proceedings

JF - British Machine Vision Conference, BMVC 2009 - Proceedings

T2 - 2009 20th British Machine Vision Conference, BMVC 2009

Y2 - 7 September 2009 through 10 September 2009

ER -