Monocular vision-based range estimation of on-road vehicles

This paper presents a monocular vision-based range estimation of on-road vehicles approach. The proposed approach mainly combines non-drivable region from drivable region detection for detection region estimation instead of detecting the whole image, shadow detection for on-road object extraction, vehicle structure points estimation and adjusting for on-road vehicle classification, and motion vector and Kalman filter of on-road vehicles for collision avoiding. Extensive experimentation was performed to demonstrate that the proposed approach can correctly and dynamically estimate the relative distance of on-road vehicles in actual traffic conditions.