@InProceedings{VictorCampmany2016,
author="Victor Campmany
and Sergio Silva
and Antonio Espinosa
and Juan Carlos Moure
and David Vazquez
and Antonio Lopez",
title="GPU-based pedestrian detection for autonomous driving",
booktitle="16th International Conference on Computational Science",
year="2016",
volume="80",
pages="2377--2381",
optkeywords="Pedestrian detection",
optkeywords="Autonomous Driving",
optkeywords="CUDA",
abstract="We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The pipeline is composed by the following state-of-the-art algorithms: Histogram of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG) features extracted from the input image; Pyramidal Sliding Window technique for foreground segmentation; and Support Vector Machine (SVM) for classification. Results show a 8x speedup in the target Tegra X1 platform and a better performance/watt ratio than desktop CUDA platforms in study.",
optnote="ADAS; 600.085; 600.082; 600.076",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2741), last updated on Wed, 11 Oct 2017 13:18:28 +0200",
opturl="https://doi.org/10.1016/j.procs.2016.05.455",
file=":http://refbase.cvc.uab.es/files/CSE2016.pdf:PDF"
}