Estimation of traffic sign visibility considering temporal environmental changes for smart driver assistance

Doman, Keisuke; Deguchi, Daisuke; Takahashi, Tomokazu; Mekada, Yoshito; Ide, Ichiro; Murase, Hiroshi; Tamatsu, Yukimasa

doi:10.1109/ivs.2011.5940467

Cited by 14 publications

(18 citation statements)

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“…Recently the models have been used to boost some computer vision and pattern recognition techniques such as object detection [113], [124], [125], object recognition [126]- [131], action recognition [132], [133], segmentation [37], [114], [115], [134], [135] and background subtraction [136]. Besides, specific applications include video summarization [137] and compression [138], scene understanding [139]- [141], computer-human interaction [98], [142]- [147], robotics [132], [148]- [150], and driver assistance [151], [152]. The potential of the visual attention models that are capable of extracting important regions promises their contributions to many other domains.…”

Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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SUMMARYWe humans are easily able to instantaneously detect the regions in a visual scene that are most likely to contain something of interest. Exploiting this pre-selection mechanism called visual attention for image and video processing systems would make them more sophisticated and therefore more useful. This paper briefly describes various computational models of human visual attention and their development, as well as related psychophysical findings. In particular, our objective is to carefully distinguish several types of studies related to human visual attention and saliency as a measure of attentiveness, and to provide a taxonomy from several viewpoints such as the main objective, the use of additional cues and mathematical principles. This survey finally discusses possible future directions for research into human visual attention and saliency computation. key words : human visual attention, computational model, saliency, bottom-up, top-down MotivationDeveloping sophisticated algorithms for detecting and recognizing something like objects from a given image and video has been a long distance challenge in pattern recognition and computer vision research fields. In fact, a huge number of studies, techniques and theories related to object detection and recognition have already been developed. In particular, several methods for detecting certain specific categories of objects such as human bodies and human faces have already been put to practical use in for example surveillance, authentication and the human-centric enhancement of image quality, with the best possible use of the prior knowledge of target objects (human bodies and faces) [1], [2]. However, generic object detection and recognition without any constraints as regards the target objects has remained major challenge, because (1) various kinds of objects might constitute the targets and (2) target objects in the same category might have different appearances due to variations of instances in a specific category, illumination changes and so on. † † The author is with the Graduate School of Informatics, Kyoto University, Kyoto-shi, 606-8501 Japan.† † † The author is with the Graduate School of Information Science, Nagoya University, Nagoya-shi, 464-8603 Japan.a) E-mail: akisato@ieee.org b) E-mail: yonetani@vision.kuee.kyoto-u.ac.jp c) E-mail: hirayama@is.nagoya-u.ac.jp DOI: 10.1587/transinf.E96.D.562 On the other hand, human beings seem to be able to detect various kinds of objects without any thought or effort. For example, from Fig. 1 left, we can easily and instantly detect a red car, a blue traffic sign and a broad white line. Visual attention [3] is considered to play an important role in achieving this function. Visual attention is one of the built-in mechanisms of the human visual system that quickly selects regions in a visual scene, which are most likely to contain items of interest. Such a pre-selection mechanism focusing only on relevant data would be essential in enabling computers to undertake subsequent processing such as generic o...

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Section: Discussionmentioning

confidence: 99%

Computational Models of Human Visual Attention and Their Implementations: A Survey

Kimura

Yonetani

Hirayama

2013

IEICE Trans. Inf. & Syst.

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“…Considering these knowledge, the proposed method first obtains a region containing a target traffic sign and its surroundings. Then, it calculates the intensity, color and edge contrasts in the region [5], and uses them as feature values c 1 , c 2 and c 3 .…”

Section: ) Local Featuresmentioning

confidence: 99%

“…({kdoman,y-mekada}@sist.chukyo-u.ac.jp) 2 Graduate School of Information Science, Nagoya University, Japan ({ide,murase}@is.nagoya-u.ac.jp) 3 Information and Communications Headquarters, Nagoya University, Japan (ddeguchi@nagoya-u.jp) 4 Faculty of Economics and Information, Gifu Shotoku Gakuen University, Japan (ttakahashi@gifu.shotoku.ac.jp) 5 DENSO CORPORATION, Japan a traffic scene. As shown in Fig.…”

Section: Introductionmentioning

confidence: 99%

“…We have proposed a visibility estimation method integrating local image features based on several kinds of contrasts around a target and its surrounding region [4], assuming the situation that a driver's gaze is around the target. As an extension to this study, we have also proposed a method that evaluates a series of instantaneous visibility values calculated from an in-vehicle camera image sequence [5]. This method still used only local image features, although it improved the accuracy of visibility estimation.…”

Section: Introductionmentioning

confidence: 99%

“…The paper concludes with a summary and future work in Section V. Figure 2 shows the process flow of the proposed system. The proposed method estimates the visibility of a traffic sign based on the framework [5] from an in-vehicle camera image sequence. First, the instantaneous visibility value of a target traffic sign is calculated by integrating both local and global features extracted from an input image for each time is averaged.…”

Section: Introductionmentioning

confidence: 99%

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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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Application of Image Recognition Technology to Vehicles

Tamatsu

Nitanda

2014

Encyclopedia of Automotive Engineering

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Image recognition technology used in driver assistance systems can be categorized into viewing cameras for displaying images from around the vehicle and sensing cameras that detect the driving environment using image recognition technology. This field is evolving rapidly due to the development of image recognition technologies, higher performance processors, and cost reductions. Recent advances in technology have enabled the statistical pattern recognition of difficult‐to‐define shapes such as vehicles, pedestrians, and the like and their practical application to driver warning and vehicle control functions.

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Estimation of traffic sign visibility considering temporal environmental changes for smart driver assistance

Cited by 14 publications

References 11 publications

Computational Models of Human Visual Attention and Their Implementations: A Survey

Computational Models of Human Visual Attention and Their Implementations: A Survey

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

Application of Image Recognition Technology to Vehicles

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