Stereo Vision-based Feature Extraction for Vehicle Detection

This paper presents a stereo vision system for vehicle detection. It has been conceived as the integration of two different subsystems. Initially a stereo vision based system is used to recover the most relevant 3D features in the scene; due to the algorithm's generality, all the vertical features are extracted as potentially belonging to a vehicle in front of the vision system. This list of significant patterns is fed to a second subsystem based on monocular vision; it processes the list computing a match with a general model of a vehicle based on symmetry and shape, thus allowing the identification of the sole characteristics belonging to a vehicle. The system presented in this work derives from the integration of the research work developed by the University of Parma (Italy) and I.N.S.A. of Rouen (France). The two subsystems have been integrated into the GOLD software and are currently under testing using the ARGO experimental vehicle.