Detection of traffic light using structural information

Omachi, Masako; Omachi, Shinichiro

doi:10.1109/icosp.2010.5655932

Cited by 57 publications

(28 citation statements)

References 8 publications

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“…Structural information is adopted by Masako Omachi and Shinichiro Omachi [12] to detect a traffic light. In particular, the disc-shaped lights are firstly modeled according to their installation position and spatial relationship, and then the pixels are clustered and normalized in the RGB color space, and finally the edges of the light regions are detected by Sobel filter.…”

Section: Related Workmentioning

confidence: 99%

Detection and classification of traffic lights for automated setup of road surveillance systems

Shi

Zhao

Xia

2014

Multimed Tools Appl

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Traffic light plays an important role in controlling the traffic flow to maintain order. The state of the traffic light is used in automatic detection of illegal motions against traffic rules. In this paper, a video based-method is proposed to tackle the problem of detection and classification of traffic lights in the scenes, thus providing an automated setup of road surveillance systems in intelligent transportation systems (ITS). Firstly, the proposed method localizes the regions of traffic lights by detecting the regularity in which the traffic light colors change, and then classify the traffic lights by an SVM classifier on their shape features. This method is insensitive to illumination changing and adaptable to various kinds of shape settings. Finally, the experimental results show that this method is efficient and effective in automatically recognizing traffic lights.

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Section: Related Workmentioning

confidence: 99%

Detection and classification of traffic lights for automated setup of road surveillance systems

Shi

Zhao

Xia

2014

Multimed Tools Appl

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“…The color can however vary from scene to scene and thus challenge models relying solely on static thresholds. So rather than looking at color, one could make use of the distinctive shape of traffic lights by applying circular Hough transform on an edge map [20] or by using radial symmetry [25]. Both approaches are challenged in different scenarios, but not entirely the same scenarios, thus shape information is fused with structural information [27,3], and additionally color information in [29,15].…”

Section: Related Workmentioning

confidence: 99%

Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data

Jensen

Nasrollahi

Moeslund

2017

2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)

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Traffic light detection (TLD) is a vital part of both intelligent vehicles and driving assistance systems (DAS). General for most TLDs is that they are evaluated on small and private datasets making it hard to determine the exact performance of a given method. In this paper we apply the state-of-the-art, real-time object detection system You Only Look Once, (YOLO) on the public LISA Traffic Light dataset available through the VIVA-challenge, which contain a high number of annotated traffic lights, captured in varying light and weather conditions. The YOLO object detector achieves an AUC of impressively 90.49 % for daysequence1, which is an improvement of 50.32 % compared to the latest ACF entry in the VIVAchallenge. Using the exact same training configuration as the ACF detector, the YOLO detector reaches an AUC of 58.3 %, which is in an increase of 18.13 %.

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“…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]. In [17,18] shape information is fused with structural information and additionally color information in [19,20].…”

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

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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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Detection of traffic light using structural information

Cited by 57 publications

References 8 publications

Detection and classification of traffic lights for automated setup of road surveillance systems

Detection and classification of traffic lights for automated setup of road surveillance systems

Evaluating State-of-the-Art Object Detector on Challenging Traffic Light Data

Comprehensive Parameter Sweep for Learning-Based Detector on Traffic Lights

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