%0 Conference Proceedings
%T GPU-based pedestrian detection for autonomous driving
%A Victor Campmany
%A Sergio Silva
%A Antonio Espinosa
%A Juan Carlos Moure
%A David Vazquez
%A Antonio Lopez
%B 16th International Conference on Computational Science
%D 2016
%V 80
%F Victor Campmany2016
%O ADAS; 600.085; 600.082; 600.076
%O exported from refbase (http://refbase.cvc.uab.es/show.php?record=2741), last updated on Wed, 11 Oct 2017 13:18:28 +0200
%X 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.
%K Pedestrian detection
%K Autonomous Driving
%K CUDA
%U https://doi.org/10.1016/j.procs.2016.05.455
%U http://refbase.cvc.uab.es/files/CSE2016.pdf
%P 2377-2381