Robust real-time traffic light detection and distance estimation using a single camera

Diaz, Moises; Cerri, Pietro; Medici, Paolo

doi:10.1016/j.eswa.2014.12.037

Cited by 94 publications

(43 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Different color spaces have also been investigated to more robustly detect the traffic lights (John et al, 2014) and multiple exposure images are tested to rigorously detect traffic lights in dark and bright environments (Jang et al, 2014). Diaz-Cabrera et al (Diaz-Cabrera et al, 2015) claim that color segmentation using fuzzy clustering can improve the traffic light detection results. In this paper, Hue, Saturation, Luminance (HSL) color space is used to detect the traffic lights since it is more resilient to illumination as opposed to Red, Green, Blue (RGB) color space.…”

Section: Previous Workmentioning

confidence: 99%

Traffic Light Detection Using Conic Section Geometry

Hosseinyalmdary

Yılmaz

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

ABSTRACT:Traffic lights detection and their state recognition is a crucial task that autonomous vehicles must reliably fulfill. Despite scientific endeavors, it still is an open problem due to the variations of traffic lights and their perception in image form. Unlike previous studies, this paper investigates the use of inaccurate and publicly available GIS databases such as OpenStreetMap. In addition, we are the first to exploit conic section geometry to improve the shape cue of the traffic lights in images. Conic section also enables us to estimate the pose of the traffic lights with respect to the camera. Our approach can detect multiple traffic lights in the scene, it also is able to detect the traffic lights in the absence of prior knowledge, and detect the traffics lights as far as 70 meters. The proposed approach has been evaluated for different scenarios and the results show that the use of stereo cameras significantly improves the accuracy of the traffic lights detection and pose estimation.

show abstract

Section: Previous Workmentioning

confidence: 99%

Traffic Light Detection Using Conic Section Geometry

Hosseinyalmdary

Yılmaz

2016

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…Gaussian distributions are calculated based on the red, amber, green, and black clusters in a large number of combinations of the RGB and RGB-N image channels. In [7] the work from [4] is expanded, by the introduction of an adaptive shutter and gain system, advanced tracking, distance estimation, and evaluate on a large and varied dataset with both day-time and night-time frames. Because of the differences in light conditions between night and day, they use one fuzzy clustering process for day conditions and another for night conditions.…”

Section: Related Workmentioning

confidence: 99%

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

Jensen

Philipsen

Bahnsen

et al. 2015

Advances in Visual Computing

View full text Add to dashboard Cite

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 %.

show abstract

“…The parameter optimization is done by adjusting one parameter at a time, e.g. creating a TL detector with a nOctUp = 0 and treeDepth = 2, and then vary the mDs size from [12,12] to [25,25]. A total of 14 2 = 196 detectors are made with above nOctUp and treeDepth settings.…”

Section: Parameter Optimizationmentioning

confidence: 99%

“…Most model-based detectors are defined by some heuristic parameters, in most cases relying on color or shape information for detecting TL candidates. The color information is used by heuristically defining thresholds for the color of interest in a given color space [12,13]. The shape information is usually found by applying circular Hough transform on an edge map [14], or finding circles by applying radial symmetry [15,16].…”

Section: Model-basedmentioning

confidence: 99%

Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

Jensen

Philipsen

Moeslund

et al. 2016

Advances in Visual Computing

View full text Add to dashboard Cite

Abstract. Determining the optimal parameters for a given detection algorithm is not straightforward and what ends up as the final values is mostly based on experience and heuristics. In this paper we investigate the influence of three basic parameters in the widely used Aggregate Channel Features (ACF) object detector applied for traffic light detection. Additionally, we perform an exhaustive search for the optimal parameters for the night time data from the LISA Traffic Light Dataset. The optimized detector reaches an Area-Under-Curve of 66.63 % on calculated precision-recall curve.

show abstract

Robust real-time traffic light detection and distance estimation using a single camera

Cited by 94 publications

References 24 publications

Traffic Light Detection Using Conic Section Geometry

Traffic Light Detection Using Conic Section Geometry

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

Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

Contact Info

Product

Resources

About