Robust vehicle detection and distance estimation under challenging lighting conditions


by M Rezaei, M Terauchi, R Klette
Abstract:
Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms.
Reference:
Robust vehicle detection and distance estimation under challenging lighting conditions (M Rezaei, M Terauchi, R Klette), In IEEE Transactions on Intelligent Transportation Systems, Institute of Electrical and Electronics Engineers Inc., volume 16, 2015.
Bibtex Entry:
@article{rezaei2015robustconditions,
author = "Rezaei, M and Terauchi, M and Klette, R",
journal = "IEEE Transactions on Intelligent Transportation Systems",
month = "May",
pages = "2723--2743",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
title = "Robust vehicle detection and distance estimation under challenging lighting conditions",
volume = "16",
year = "2015",
abstract = "Avoiding high computational costs and calibration issues involved in stereo-vision-based algorithms, this paper proposes real-time monocular-vision-based techniques for simultaneous vehicle detection and inter-vehicle distance estimation, in which the performance and robustness of the system remain competitive, even for highly challenging benchmark datasets. This paper develops a collision warning system by detecting vehicles ahead and, by identifying safety distances to assist a distracted driver, prior to occurrence of an imminent crash. We introduce adaptive global Haar-like features for vehicle detection, tail-light segmentation, virtual symmetry detection, intervehicle distance estimation, as well as an efficient single-sensor multifeature fusion technique to enhance the accuracy and robustness of our algorithm. The proposed algorithm is able to detect vehicles ahead at both day or night and also for short- and long-range distances. Experimental results under various weather and lighting conditions (including sunny, rainy, foggy, or snowy) show that the proposed algorithm outperforms state-of-the-art algorithms.",
doi = "10.1109/TITS.2015.2421482",
issn = "1524-9050",
issue = "5",
language = "eng",
day = "4",
publicationstatus = "accepted",
}