@inproceedings{93a9bb2314164c27a8de4df5bad47a9e,
title = "A robust algorithm for the detection of vehicle turn signals and brake lights",
abstract = "Robust and lightweight detection of alert signals of front vehicle, such as turn signals and brake lights, is extremely critical, especially in autonomous vehicle applications. Even with cars that are driven by human beings, automatic detection of these signals can aid in the prevention of otherwise deadly accidents. This paper presents a novel, robust and lightweight algorithm for detecting brake lights and turn signals both at night and during the day. The proposed method employs a Kalman filter to reduce the processing load. Much research is focused only on the detection brake lights at night, but our algorithm is able to detect turn signals as well as brake lights under any lighting conditions with high accuracy rates.",
keywords = "Autonomous vehicles, Cameras, Kalman filter, Signal processing algorithms, Tracking, Transportation, Vehicle light detection",
author = "Mauricio Casares and Akhan Almagambetov and Senem Velipasalar",
year = "2012",
doi = "10.1109/AVSS.2012.2",
language = "English (US)",
isbn = "9780769547978",
series = "Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012",
pages = "386--391",
booktitle = "Proceedings - 2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012",
note = "2012 IEEE 9th International Conference on Advanced Video and Signal-Based Surveillance, AVSS 2012 ; Conference date: 18-09-2012 Through 21-09-2012",
}