PT Unknown
AU Gabriel Villalonga
   Sebastian Ramos
   German Ros
   David Vazquez
   Antonio Lopez
AU ECCV-Demo
TI 3d Pedestrian Detection via Random Forest
PY 2014
DE Pedestrian Detection
AB 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.
ER