An integrated framework of vision-based vehicle detection with knowledge fusion

Abstract

This paper describes an integrated framework of on-road vehicle detection through knowledge fusion. In contrast to appearance-based detectors that make instant decisions, the proposed detection framework fuses appearance, geometry and motion information over multiple image frames. The knowledge of vehicle/non-vehicle appearance, scene geometry and vehicle motion is utilized through prior models obtained by learning, modeling and estimation algorithms. It is shown that knowledge fusion largely improves the robustness and reliability of the detection system.