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Author (up) Victor Campmany; Sergio Silva; Juan Carlos Moure; Toni Espinosa; David Vazquez; Antonio Lopez edit   pdf
openurl 
  Title GPU-based pedestrian detection for autonomous driving Type Conference Article
  Year 2016 Publication GPU Technology Conference Abbreviated Journal  
  Volume Issue Pages  
  Keywords Pedestrian Detection; 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.  
  Address Silicon Valley; San Francisco; USA; April 2016  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference GTC  
  Notes ADAS; 600.085; 600.082; 600.076 Approved no  
  Call Number ADAS @ adas @ CSM2016 Serial 2737  
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