@InProceedings{FahadShahbazKhan2012,
author="Fahad Shahbaz Khan
and Muhammad Anwer Rao
and Joost Van de Weijer
and Andrew Bagdanov
and Maria Vanrell
and Antonio Lopez",
title="Color Attributes for Object Detection",
booktitle="25th IEEE Conference on Computer Vision and Pattern Recognition",
year="2012",
publisher="IEEE Xplore",
pages="3306--3313",
optkeywords="pedestrian detection",
abstract="State-of-the-art object detectors typically use shape information as a low level feature representation to capture the local structure of an object. This paper shows that early fusion of shape and color, as is popular in image classification,leads to a significant drop in performance for object detection. Moreover, such approaches also yields suboptimal results for object categories with varying importance of color and shape.In this paper we propose the use of color attributes as an explicit color representation for object detection. Color attributes are compact, computationally efficient, and when combined with traditional shape features provide state-ofthe-art results for object detection. Our method is tested on the PASCAL VOC 2007 and 2009 datasets and results clearly show that our method improves over state-of-the-art techniques despite its simplicity. We also introduce a new dataset consisting of cartoon character images in which color plays a pivotal role. On this dataset, our approach yields a significant gain of 14\% in mean AP over conventional state-of-the-art methods.",
optnote="ADAS; CIC;",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=1935), last updated on Fri, 04 Feb 2022 12:58:58 +0100",
isbn="978-1-4673-1226-4",
issn="1063-6919",
doi="10.1109/CVPR.2012.6248068",
opturl="http://ieeexplore.ieee.org/xpl/articleDetails.jsp? arnumber=6248068",
file=":http://refbase.cvc.uab.es/files/KRW2012.pdf:PDF"
}