Monocular vision-based range estimation of on-road vehicles

Author

Chih-Ming Hsu ; Fei-Hong Chao ; Feng-Li Lian

Author_Institution

Dept. of Mech. Eng., Nat. Taipei Univ. of Technol., Taipei, Taiwan

fYear

2014

fDate

11-13 July 2014

Firstpage

94

Lastpage

98

Abstract

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.

Keywords

Kalman filters; collision avoidance; computer vision; feature extraction; image classification; image motion analysis; object detection; road vehicles; traffic engineering computing; Kalman filter; collision avoidance; drivable region detection; image detection; monocular vision-based range estimation; motion vector; nondrivable region detection; on-road object extraction; on-road vehicle classification; on-road vehicles approach; shadow detection; traffic conditions; vehicle structure points estimation; Artificial intelligence; Optical filters; Optical imaging; Roads; Vehicles;

fLanguage

English

Publisher

ieee

Conference_Titel

System Science and Engineering (ICSSE), 2014 IEEE International Conference on

Conference_Location

Shanghai

Type

conf

DOI

10.1109/ICSSE.2014.6887912

Filename

6887912