Shadow-based vehicle detection in urban traffic

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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Ibarra-Arenado M., Tjahjadi T., Pérez-Oria J., Robla-Gómez S., Jiménez-Avello A.,

 Fuente: Sensors, 2017, 17(5), 975

Editorial: MDPI

 Fecha de publicación: 27/04/2017

Nº de páginas: 19

Tipo de publicación: Artículo de Revista

DOI: 10.3390/s17050975

ISSN: 1424-8220

Proyecto español: DPI2012-36959