PT Unknown
AU Diego Cheda
   Daniel Ponsa
   Antonio Lopez
TI Pedestrian Candidates Generation using Monocular Cues
BT IEEE Intelligent Vehicles Symposium
PY 2012
BP 7
EP 12
DI 10.1109/IVS.2012.6232117
DE pedestrian detection
AB Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached.
ER