Citation

Ibarra-Arenado M, Tjahjadi T, Pérez-Oria J, Robla-Gómez S, Jiménez-Avello A. Sensors (Basel) 2017; 17(5): s17050975.

Affiliation

Department of Automatics, Electronic Engineering and Industrial Computing at the Polytechnic University of Madrid, 28006 Madrid, Spain. agustin.jimenez@upm.es.

Copyright

(Copyright © 2017, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s17050975

PMID

28448465

Abstract

Vehicle detection is a fundamental task in Forward Collision Avoiding Systems (FACS). Generally, vision-based vehicle detection methods consist of two stages: hypotheses generation and hypotheses verification. In this paper, we focus on the former, presenting a feature-based method for on-road vehicle detection in urban traffic. Hypotheses for vehicle candidates are generated according to the shadow under the vehicles by comparing pixel properties across the vertical intensity gradients caused by shadows on the road, and followed by intensity thresholding and morphological discrimination. Unlike methods that identify the shadow under a vehicle as a road region with intensity smaller than a coarse lower bound of the intensity for road, the thresholding strategy we propose determines a coarse upper bound of the intensity for shadow which reduces false positives rates. The experimental results are promising in terms of detection performance and robustness in day time under different weather conditions and cluttered scenarios to enable validation for the first stage of a complete FACS.

Language: en

Keywords

driving assistance systems; forward collision avoidance systems; shadow detection; vehicle detection