Rezaei, M orcid.org/0000-0003-3892-421X, Terauchi, M and Klette, R (2015) Robust Vehicle Detection and Distance Estimation Under Challenging Lighting Conditions. IEEE Transactions on Intelligent Transportation Systems, 16 (5). pp. 2723-2743. ISSN 1524-9050
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.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2015 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Uploaded in accordance with the publisher's self-archiving policy. |
Keywords: | Advanced driver assistance systems; vehicle detection; distance estimation; tail-light segmentation; symmetry detection; challenging lighting condition; global Haar-like features; horizontal edge detection; rear-end crashes; collision avoidance; Dempster–Shafer fusion |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > Institute for Transport Studies (Leeds) > ITS: Safety and Technology (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Sep 2020 14:52 |
Last Modified: | 22 Sep 2020 14:52 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers |
Identification Number: | 10.1109/tits.2015.2421482 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:164835 |