Vision-Based Vehicle Detection for a Forward Collision Warning System

摘要

style="text-align:justify;"> A weather-adaptive forward collision warning (FCW) system was presented by applying local features for vehicle detection and global features for vehicle verification. In the system, horizontal and vertical edge maps are separately calculated. Then edge maps are threshold by an adaptive threshold value to adapt the brightness variation. Third, the edge points are linked to generate possible objects. Fourth, the objects are judged based on edge response, location, and symmetry to generate vehicle candidates. At last, a method based on the principal component analysis (PCA) is proposed to verify the vehicle candidates. The proposed FCW system has the following properties: 1) the edge extraction is adaptive to various lighting condition; 2) the local features are mutually processed to improve the reliability of vehicle detection; 3) the hierarchical schemes of vehicle detection enhance the adaptability to various weather conditions; 4) the PCA-based verification can strictly eliminate the candidate regions without vehicle appearance.