A multi-feature fusion based traffic light recognition algorithm for intelligent vehicles

Advances in Visual Computing

Bahnsen

et al. 2015

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 %.

Section: Detection By Thresholdingmentioning

confidence: 87%

“…Spotlight detection using the white top hat operation on intensity images is seen in [12,13,14] and [15]. In [16], the V channel from the HSV color space is used with the same effect. A high proportion of publications use simple thresholding of color channels in some form.…”

Section: Related Workmentioning

confidence: 99%

Traffic Light Detection at Night: Comparison of a Learning-Based Detector and Three Model-Based Detectors

Advances in Visual Computing

Bahnsen

et al. 2015

“…This method has been used in a significant fraction of recent TLR papers [11], [9], [12], [13], [19]. The found spotlight are scored based on the listed metrics.…”

Section: B Heuristic Model Based Detectionmentioning

confidence: 99%

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

2015 IEEE 18th International Conference on Intelligent Transportation Systems

Møgelmose

et al. 2015

Abstract-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.

“…The detector reaches a detection rate of more than 90%. The detection rate is described as the ratio between correct TL recognitions and the ground truth in a given video sequence [19] begins by detecting the vanishing line and thereby reducing the search area considerably, relying on the assumption that TLs will only appear above this line. They then apply the white top-hat operation, similarly to [17,2,5].…”

Section: Related Workmentioning

confidence: 99%

Ongoing work on traffic lights: Detection and evaluation

2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

Trivedi

et al. 2015

Research in traffic light recognition (TLR) has stagnated compared to related computer vision areas, such as pedestrian detection and and traffic sign recognition. We focus on the detection sub-problem, since this is the most challenging problem and solving this is the key to a successful TLR system. This is done by looking at four detectors from different author groups and their reported results. From surveying existing work it is clear that currently evaluation is limited primarily to small local datasets. In order to provide a common basis for future comparison of TLR research an extensive public database is collected based on footage from US roads. The database consists of continuous test and training video sequences, totaling 46,418 frames and 112,971 annotated traffic lights. The sequences are captured by a stereo camera mounted on the roof of a vehicle driving under both night and day conditions with varying light and weather.