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Author (up) Gabriel Villalonga; Sebastian Ramos; German Ros; David Vazquez; Antonio Lopez edit  openurl
  Title 3d Pedestrian Detection via Random Forest Type Miscellaneous
  Year 2014 Publication European Conference on Computer Vision Abbreviated Journal  
  Volume Issue Pages 231-238  
  Keywords Pedestrian Detection  
  Abstract Our demo focuses on showing the extraordinary performance of our novel 3D pedestrian detector along with its simplicity and real-time capabilities. This detector has been designed for autonomous driving applications, but it can also be applied in other scenarios that cover both outdoor and indoor applications.
Our pedestrian detector is based on the combination of a random forest classifier with HOG-LBP features and the inclusion of a preprocessing stage based on 3D scene information in order to precisely determinate the image regions where the detector should search for pedestrians. This approach ends up in a high accurate system that runs real-time as it is required by many computer vision and robotics applications.		  
  Address Zurich; suiza; September 2014  
  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 ECCV-Demo  
  Notes ADAS; 600.076 Approved no  
  Call Number Admin @ si @ VRR2014 Serial 2570  
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