Measurement of Visibility Conditions toward Smart Driver Assistance for Traffic Signals

Kimura, Fumitaka; Takahashi, Tomokazu; Mekada, Yoshito; Ide, Ichiro; Murase, Hiroshi; Miyahara, Takayuki; Tamatsu, Yukimasa

doi:10.1109/ivs.2007.4290187

Cited by 14 publications

(14 citation statements)

References 8 publications

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“…In addition, evaluating edge contrast improves the accuracy of visibility estimation for a driving scene with complex background texture [11]. Considering these knowledge, the proposed method first obtains a region containing a target traffic sign and its surroundings.…”

Section: ) Local Featuresmentioning

confidence: 99%

Estimation of traffic sign visibility considering local and global features in a driving environment

Doman

Deguchi

Takahashi

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

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Abstract-This paper proposes a camera-based visibility estimation method for a traffic sign. The visibility here indicates how a visual target is easy to be detected and recognized by a human driver (not a machine). This research aims at realizing a nuisance-free driver assistance system which sorts out information depending on the visibility of a visual target, in order to prevent driver distraction. Our previous study on estimating the visibility of a traffic sign considered only the effect of the local region around a target, assuming the situation that a driver's gaze is around it. The proposed method integrates both the local features and global features in a driving environment without such an assumption. The global features evaluate the positional relationships between traffic signs and the appearance around the fixation point of a driver's gaze, which considers the effect of the driver's entire field of view. Experimental results showed the effectiveness of incorporating the global features for estimating the visibility of a traffic sign.

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Section: ) Local Featuresmentioning

confidence: 99%

Estimation of traffic sign visibility considering local and global features in a driving environment

Doman

Deguchi

Takahashi

et al. 2014

2014 IEEE Intelligent Vehicles Symposium Proceedings

Self Cite

View full text Add to dashboard Cite

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“…In contrast, some research groups proposed methods for estimating the visibility of specific objects, such as traffic signals, traffic signs, and pedestrians. As described above, Kimura et al proposed a method for estimating the visibility of traffic signals at daytime and nighttime under fine weather conditions [1]. They employed texture features calculated from the contrast between a traffic signal and its surroundings, and reported the effectiveness for the visibility estimation under fine weather conditions.…”

Section: Related Workmentioning

confidence: 99%

“…As reported in [1], it is easy to detect a traffic signal for drivers if it has a different texture from its surroundings. That is, the visibility of a traffic signal tends to increase in proportion to the degree of the texture difference from its surrounding.…”

Section: B Computation Of Texture Featurementioning

confidence: 99%

“…Therefore, this paper introduces visual noise features that significantly affect the visibility of traffic signals. The proposed method estimates the visibility of traffic signals for drivers by image processing with visual noise features in combination with the texture feature introduced in the previous work for fine weather conditions [1]. In this way, we expect to improve the accuracy of visibility estimation of traffic signals under rainy weather conditions.…”

Section: Introductionmentioning

confidence: 95%

“…Thus, we focus on a method to estimate the visibility of traffic signals for drivers under rainy weather conditions. Kimura et al proposed a method for estimating the visibility of traffic signals by evaluating the contrast of image features between a traffic signal and its surroundings [1]. They evaluated their method with in-vehicle camera images captured at daytime and nighttime under fine weather conditions.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Visibility estimation of traffic signals under rainy weather conditions for smart driving support

Sato

Domany

Deguchi

et al. 2012

2012 15th International IEEE Conference on Intelligent Transportation Systems

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The aim of this work is to support a driver by notifying the information of traffic signals in accordance with their visibility. To avoid traffic accidents, the driver should detect and recognize surrounding objects, especially traffic signals. However, when driving a vehicle under rainy weather conditions, it is difficult for drivers to detect or to recognize objects existing in the road environment in comparison with fine weather conditions. Therefore, this paper proposes a method for estimating the visibility of traffic signals for drivers under rainy weather conditions by image processing. The proposed method is based on the concept of visual noise known in the field of cognitive science, and extracts two types of visual noise features which ware considered that they affect the visibility of traffic signals. We expect to improve the accuracy of visibility estimation by combining the visual noise features with the texture feature introduced in a previous work. Experimental results showed that the proposed method could estimate the visibility of traffic signals more accurately under rainy weather conditions.

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The range of visual detection of ground-level cues during distracted walking: Effect of cue contrast and walking speed

Kim

Kwon

Kim

et al. 2022

Accident Analysis & Prevention

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Measurement of Visibility Conditions toward Smart Driver Assistance for Traffic Signals

Cited by 14 publications

References 8 publications

Estimation of traffic sign visibility considering local and global features in a driving environment

Estimation of traffic sign visibility considering local and global features in a driving environment

Visibility estimation of traffic signals under rainy weather conditions for smart driving support

The range of visual detection of ground-level cues during distracted walking: Effect of cue contrast and walking speed

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