@InProceedings{AlejandroGonzalezAlzate2015,
author="Alejandro Gonzalez Alzate
and Gabriel Villalonga
and German Ros
and David Vazquez
and Antonio Lopez",
title="3D-Guided Multiscale Sliding Window for Pedestrian Detection",
booktitle="Pattern Recognition and Image Analysis, Proceedings of 7th Iberian Conference , ibPRIA 2015",
year="2015",
volume="9117",
pages="560--568",
optkeywords="Pedestrian Detection",
abstract="The most relevant modules of a pedestrian detector are the candidate generation and the candidate classification. The former aims at presenting image windows to the latter so that they are classified as containing a pedestrian or not. Much attention has being paid to the classification module, while candidate generation has mainly relied on (multiscale) sliding window pyramid. However, candidate generation is critical for achieving real-time. In this paper we assume a context of autonomous driving based on stereo vision. Accordingly, we evaluate the effect of taking into account the 3D information (derived from the stereo) in order to prune the hundred of thousands windows per image generated by classical pyramidal sliding window. For our study we use a multimodal (RGB, disparity) and multi-descriptor (HOG, LBP, HOG+LBP) holistic ensemble based on linear SVM. Evaluation on data from the challenging KITTI benchmark suite shows the effectiveness of using 3D information to dramatically reduce the number of candidate windows, even improving the overall pedestrian detection accuracy.",
optnote="ADAS; 600.076; 600.057; 600.054",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2585), last updated on Thu, 10 Nov 2016 12:47:07 +0100",
doi="10.1007/978-3-319-19390-8_63",
file=":http://refbase.cvc.uab.es/files/GVR2015.pdf:PDF"
}