Abstract
Traffic light recognition (TLR) is an integral part of any intelligent 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 evaluate 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 outperforms 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 %.
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Notes
- 1.
Freely available at http://cvrr.ucsd.edu/LISA/datasets.html for educational, research, and non-profit purposes.
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Jensen, M.B., Philipsen, M.P., Bahnsen, C., Møgelmose, A., Moeslund, T.B., Trivedi, M.M. (2015). Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9474. Springer, Cham. https://doi.org/10.1007/978-3-319-27857-5_69
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