@InProceedings{VictorCampmany2016,
author="Victor Campmany
and Sergio Silva
and Juan Carlos Moure
and Toni Espinosa
and David Vazquez
and Antonio Lopez",
title="GPU-based pedestrian detection for autonomous driving",
booktitle="GPU Technology Conference",
year="2016",
optkeywords="Pedestrian Detection",
optkeywords="GPU",
abstract="Pedestrian detection for autonomous driving is one of the hardest tasks within computer vision, and involves huge computational costs. Obtaining acceptable real-time performance, measured in frames per second (fps), for the most advanced algorithms is nowadays a hard challenge. Taking the work in [1] as our baseline, we propose a CUDA implementation of a pedestrian detection system that includes LBP and HOG as feature descriptors and SVM and Random forest as classifiers. We introduce significant algorithmic adjustments and optimizations to adapt the problem to the NVIDIA GPU architecture. The aim is to deploy a real-time system providing reliable results.",
optnote="ADAS; 600.085; 600.082; 600.076",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2737), last updated on Wed, 06 Jul 2016 12:58:19 +0200",
file=":http://refbase.cvc.uab.es/files/CSM2016.pdf:PDF"
}