by Rezaei, M and Klette, R
Abstract:
The paper develops a novel technique that significantly improves the performance of Haar-like feature-based object detectors in terms of speed, detection rate under difficult lighting conditions, and reduced number of false-positives. The method is implemented and validated for driver monitoring under very dark, very bright, and normal conditions. The framework includes a fast adaptive detector designed to cope with rapid lighting variations, as well as an implementation of a Kalman filter for reducing the search region and indirect support of eye monitoring and tracking. The proposed methodology effectively works under low-light conditions without using infrared illumination or any other extra lighting support. Experimental results, performance evaluation, and comparing a standard Haar-like detector with the proposed adaptive eye detector, show noticeable improvements. © 2013 Springer-Verlag.
Reference:
Novel adaptive eye detection and tracking for challenging lighting conditions (Rezaei, M and Klette, R), In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), volume 7729 LNCS, 2013.
Bibtex Entry:
@inproceedings{rezaei2013novelconditions,
author = "Rezaei, M and Klette, R",
booktitle = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
pages = "427--440",
title = "Novel adaptive eye detection and tracking for challenging lighting conditions",
volume = "7729 LNCS",
year = "2013",
abstract = "The paper develops a novel technique that significantly improves the performance of Haar-like feature-based object detectors in terms of speed, detection rate under difficult lighting conditions, and reduced number of false-positives. The method is implemented and validated for driver monitoring under very dark, very bright, and normal conditions. The framework includes a fast adaptive detector designed to cope with rapid lighting variations, as well as an implementation of a Kalman filter for reducing the search region and indirect support of eye monitoring and tracking. The proposed methodology effectively works under low-light conditions without using infrared illumination or any other extra lighting support. Experimental results, performance evaluation, and comparing a standard Haar-like detector with the proposed adaptive eye detector, show noticeable improvements. © 2013 Springer-Verlag.",
doi = "10.1007/978-3-642-37484-5_35",
isbn = "9783642374838",
issn = "0302-9743",
eissn = "1611-3349",
issue = "PART 2",
language = "eng",
}