Robust recognition of traffic signals

Lindner, Frank; Kreßel, Ulrich; Kaelberer, S.

doi:10.1109/ivs.2004.1336354

Cited by 87 publications

(39 citation statements)

References 3 publications

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“…Gupta works. The majority of these works have focused purely on image recognition [25,34,22]. Of particular interest, however, are those that seek to minimize the risk posed by errors inherent to vision-based detection systems.…”

Section: Summary Of Publicationsmentioning

confidence: 99%

Traffic light status detection using movement patterns of vehicles

Campbell

Amor

Ang

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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Traditional methods for detecting the status of traffic lights used in autonomous vehicles may be susceptible to errors, which is troublesome in a safety-critical environment. In the case of vision-based recognition methods, failures may arise due to disturbances in the environment such as occluded views or poor lighting conditions. Some methods also depend on high-precision meta-data which is not always avail-

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Section: Summary Of Publicationsmentioning

confidence: 99%

Traffic light status detection using movement patterns of vehicles

Campbell

Amor

Ang

et al. 2016

2016 IEEE 19th International Conference on Intelligent Transportation Systems (ITSC)

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“…One of the first learning-based detectors is introduced in [22,23] where a cascading classifier is tested using Haar-like features, but was unable to perform better than their Gaussian color classifier. The popular combination of Histogram of Oriented Gradients (HoG) features and SVM classifier were introduced in [24], but additionally also relying on prior maps with very precise knowledge of the TL locations.…”

Section: Learning-basedmentioning

confidence: 99%

Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

Jensen

Philipsen

Moeslund

et al. 2016

Advances in Visual Computing

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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.

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“…Today, with top quality highways and speedy vehicles, there is a more stringent need for abiding by tra c rules than ever. Therefore, vehicles being able to detect a tra c board and inform the driver can be very helpful in reducing road accidents [3,4].…”

Section: Introductionmentioning

confidence: 99%