GPU-based pedestrian detection for autonomous driving

V. Campmany, S. Silva, A. Espinosa, J.C. Moure, David Vázquez-Loureiro, Antonio M. López

Research output: Chapter in BookChapterResearchpeer-review

22 Citations (Scopus)


We propose a real-time pedestrian detection system for the embedded Nvidia Tegra X1 GPU-CPU hybrid platform. The detection pipeline is composed by the following state-of-the-art algorithms: features extracted from the input image are Histograms of Local Binary Patterns (LBP) and Histograms of Oriented Gradients (HOG); candidate generation using Pyramidal Sliding Window technique; and classification with Support Vector Machine (SVM). Experimental results show that the Tegra ARM platform is two times more energy efficient than a desktop GPU and at least 8 times faster than a desktop multicore CPU.
Original languageEnglish
Title of host publicationProcedia Computer Science
Number of pages5
Publication statusPublished - 2016

Publication series

NameInternational Conference on Computational Science 2016, ICCS 2016
PublisherProcedia Computer Science
ISSN (Print)1877-0509


  • Autonomous Driving
  • pedestrian detection
  • Computer Vision


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