Traffic Light Detection: A Learning Algorithm and Evaluations on Challenging Dataset

Mark Philip Philipsen, Morten Bornø Jensen, Andreas Møgelmose, Thomas B. Moeslund, Mohan M. Trivedi

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Traffic light recognition (TLR) is an integral part of any intelligent vehicle, which must function in the existing infrastructure. Pedestrian and sign detection have recently seen great improvements due to the introduction of learning based detectors using integral channel features. A similar push have not yet been seen for the detection sub-problem of TLR, where detection is dominated by methods based on heuristic models.
Evaluation of existing systems is currently limited primarily to small local datasets. In order to provide a common basis for comparing future TLR research an extensive public database is collected based on footage from US roads. The database consists of both test and training data, totaling 46,418 frames and 112,971 annotated traffic lights, captured in continuous sequences under a varying light and weather conditions.
The learning based detector achieves an AUC of 0.4 and 0.32 for day sequence 1 and 2, respectively, which is more than an order of magnitude better than the two heuristic model-based detectors.
Titel2015 IEEE 18th International Conference on Intelligent Transportation Systems (ITSC 2015) : Proceedings
ISBN (Trykt)978-1-4673-6595-6
StatusUdgivet - 2015
Begivenhed18th IEEE Intelligent Transportation Systems Conference - Las Palmas de Gran Canaria, Spanien
Varighed: 15 sep. 201518 sep. 2015
Konferencens nummer: 18


Konference18th IEEE Intelligent Transportation Systems Conference
ByLas Palmas de Gran Canaria
NavnI E E E Intelligent Transportation Systems Conference. Proceedings

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