Implementation of road traffic signs detection based on saliency map model

Won, Woong-Jae; Son, Joonwoo

doi:10.1109/ivs.2008.4621144

Cited by 15 publications

(7 citation statements)

References 13 publications

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“…In order to overcome the problems arisen in complex environment, some researchers [38,32,13,18] have considered color visual saliency techniques which are based on biologically motivated selective attention mechanism to imitate human-like early visual processing.…”

Section: Related Work Using Color Segmentationmentioning

confidence: 99%

“…In existing research, many researchers have used saliency-map models to simulate the visual attention mechanism (e.g. [13,18,32,38]). In [13], colour appearance model CIECAM97 has been applied to extract colour information.…”

Section: The Segmentation Modulementioning

confidence: 99%

“…However, it can not distinguish these objects similar to road signs. Woong-Jae [38] considered the color opponents and their edge information with center surrounding difference and normalization as a pre-processing, which is effective to intensify the sign board color characteristics as well as reducing background noise influence. In real environment, the proposed model can successfully localizes a salient area and detects the road traffic sign regions.…”

Section: The Segmentation Modulementioning

confidence: 99%

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Fast and robust road sign detection in driver assistance systems

2018

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Road sign detection plays a critical role in automatic driver assistance systems. Road signs possess a number of unique visual qualities in images due to their specific colors and symmetric shapes. In this paper, road signs are detected by a two-level hierarchical framework that considers both color and shape of the signs. To address the problem of low image contrast, we propose a new color segmentation algorithm based on color visual saliency, which uses the ratios of enhanced and normalized color values to capture color information. To improve computation efficiency and reduce false alarm rate, we modify the fast radial symmetry transform (RST) algorithm, and propose to use an edge pairwise voting scheme to group feature points based on their underlying symmetry in the candidate regions. Experimental results on several benchmarking datasets demonstrate the superiority of our method over the state-of-the-arts on both efficiency and robustness.

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Section: Related Work Using Color Segmentationmentioning

confidence: 99%

Section: The Segmentation Modulementioning

confidence: 99%

Section: The Segmentation Modulementioning

confidence: 99%

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Fast and robust road sign detection in driver assistance systems

2018

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“…Herein we discuss the road sign detection methods that are based on visual saliency. Recently, saliency detection has attracted more attention in computer vision because of its wide application [8], [14]. Itti et al proposed a saliency model that computes saliency in each of several feature channels (e.g., color, intensity, orientation; saliency is then the relative difference between a region and its surrounding) in parallel and fuses them in a scalar map called the 'saliency map' [15].…”

Section: Road Sign Detectionmentioning

confidence: 99%

“…Woong-Jae Won et al proposed a road sign detection method based on the main idea of Itti's model. Because the road traffic signboards have dominant color contrast against backgrounds, they consider the color opponents and its edge information with center surround difference and normalization as a pre-processing before the saliency map is generated [14], [16]. Anh Cat Le Ngo et al used traffic-sign colors to tune the bottom-up visual saliency from general object detection to traffic-sign detection [7].…”

Section: Road Sign Detectionmentioning

confidence: 99%

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Wei

Tang

et al. 2014

IEICE Trans. Inf. & Syst.

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SUMMARYIn this paper, we propose a novel method for road sign detection and recognition in complex scene real world images. Our algorithm consists of four basic steps. First, we employ a regional contrast based bottom-up visual saliency method to highlight the traffic sign regions, which usually have dominant color contrast against the background. Second, each type of traffic sign has special color distribution, which can be explored by top-down visual saliency to enhance the detection precision and to classify traffic signs into different categories. A bag-of-words (BoW) model and a color name descriptor are employed to compute the specialclass distribution. Third, the candidate road sign blobs are extracted from the final saliency map, which are generated by combining the bottom-up and the top-down saliency maps. Last, the color and shape cues are fused in the BoW model to express blobs, and a support vector machine is employed to recognize road signs. Experiments on real world images show a high success rate and a low false hit rate and demonstrate that the proposed framework is applicable to prohibition, warning and obligation signs. Additionally, our method can be applied to achromatic signs without extra processing.

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Improving the efficiency and accuracy of visual attention

Fernández-Carbajales

García

Martinez

2011

2011 8th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS)

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Visual attention is the cognitive process of selectively focusing on certain areas of a visual scene while ignoring the others. It is a desirable capability for intelligent video surveillance systems, as it allows them to control the aim of mobile cameras or to selectively process the most relevant parts of the captured images. This paper proposes an adaptation of a well-known biologicallyinspired visual attention model in order to increase its computational efficiency without sacrificing its accuracy, and shows that the latter can be further improved through a supervised training stage that fine-tunes the model to the particular application scope in which the system is being utilized. Experimental results and comparisons with previous visual attention techniques are shown and discussed.

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Implementation of road traffic signs detection based on saliency map model

Cited by 15 publications

References 13 publications

Fast and robust road sign detection in driver assistance systems

Fast and robust road sign detection in driver assistance systems

Exploiting Visual Saliency and Bag-of-Words for Road Sign Recognition

Improving the efficiency and accuracy of visual attention

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