@inproceedings{9db6a35f8efb4dc59fd953fdd25cdfa7,
title = "Traffic Light Detection at Night: Comparison of a Learning-based Detector and three Model-based Detectors",
abstract = "Traffic light recognition (TLR) is an integral part of any in- telligent vehicle, it must function both at day and at night. However, the majority of TLR research is focused on day-time scenarios. In this paper we will focus on detection of traffic lights at night and evalu- ate the performance of three detectors based on heuristic models and one learning-based detector. Evaluation is done on night-time data from the public LISA Traffic Light Dataset. The learning-based detector out- performs the model-based detectors in both precision and recall. The learning-based detector achieves an average AUC of 51.4 % for the two night test sequences. The heuristic model-based detectors achieves AUCs ranging from 13.5 % to 15.0 %.",
author = "Jensen, {Morten Born{\o}} and Philipsen, {Mark Philip} and Chris Bahnsen and Andreas M{\o}gelmose and Moeslund, {Thomas B.} and Trivedi, {Mohan M.}",
year = "2015",
month = oct,
doi = "10.1007/978-3-319-27857-5_69",
language = "English",
isbn = "978-3-319-27856-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "774--783",
booktitle = "Advances in Visual Computing",
address = "Germany",
note = "ISVC 2015 ; Conference date: 14-12-2015 Through 16-12-2015",
}