A Novel Color Descriptor for Road-Sign Detection

Zhang, Qieshi; Kamata, Sei‐ichiro

doi:10.1587/transfun.e96.a.971

Cited by 6 publications

(15 citation statements)

References 19 publications

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“…Although the previous proposed CBM-BD separation approaches in PCS [8] and RCS [9], [10] can obtain acceptable result, they have some shortcomings as shown in above. For overcoming these shortcomings, the CBM-BD is improved to CCS and a constrained clustering method is studied to obtain the color separation result more accurate.…”

Section: Improved Cbm In Ccs and Its Separationmentioning

confidence: 98%

“…After that, for improving the separation strategy, the adaptive thresholds selection method is proposed [9], [10]. This section introduces the original CBM briefly, and for more details refer the previous work [10].…”

Section: Original Color Barycenter Model and Its Significantmentioning

confidence: 99%

“…Therefore, the weaker color region cannot be detected in complex surroundings sometimes. Although, the previous proposed CBM based segmentation methods [9], [10] can obtain the effective results, they never considered the influence of number with same color and the separation between magenta and blue regions is not accurate in some situations. In addition, sometimes the previous methods cannot obtain the correct result by the limitation of separation method.…”

Section: Introductionmentioning

confidence: 99%

“…Bahlmann et al [7] calculated the color feature by non-linear color transform to seven color representations. After that, our previous work [8] proposed the original color barycenter model (CBM) which called as color barycenter hexagon (CBH) model to analyze the color feature by the RGB color space conversion and improved it in [9], [10]. Tsai et al [11] projected the RGB color space as the Eigen by principal component analysis (PCA), and used radial basis function NN to train the classifier for detection.…”

Section: Introductionmentioning

confidence: 99%

“…The CBM-BD is covered into cylindrical coordinate system (CCS) as the improvement of CBM for analyzing the color feature more accurate and easier. In our previous research, the CBM-BD is described in polar coordinate system (PCS) [8] and rectangular coordinate system (RCS) [9], [10]. However, although in PCS and RCS with the corresponding segmentation approach can obtain the acceptable result, some shortcomings need be solved.…”

Section: Introductionmentioning

confidence: 99%

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Improved Color Barycenter Model and Its Separation for Road Sign Detection

Zhang

Kamata

2013

IEICE Trans. Inf. & Syst.

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Qieshi ZHANG†a) , Student Member and Sei-ichiro KAMATA †b) , Member SUMMARY This paper proposes an improved color barycenter model (CBM) and its separation for automatic road sign (RS) detection. The previous version of CBM can find out the colors of RS, but the accuracy is not high enough for separating the magenta and blue regions and the influence of number with the same color are not considered. In this paper, the improved CBM expands the barycenter distribution to cylindrical coordinate system (CCS) and takes the number of colors at each position into account for clustering. Under this distribution, the color information can be represented more clearly for analyzing. Then aim to the characteristic of barycenter distribution in CBM (CBM-BD), a constrained clustering method is presented to cluster the CBM-BD in CCS. Although the proposed clustering method looks like conventional K-means in some part, it can solve some limitations of K-means in our research. The experimental results show that the proposed method is able to detect RS with high robustness. key words: road sign (RS) detection, driver assistance system (DAS), color triangle, color barycenter model (CBM), constrained clustering IntroductionThe research of road sign (RS) detection becomes more and more important for driver navigation. Because it can regulate traffic and indicate the road situation for guidance and warning, which is a crucial part function of driver assistance systems (DAS). For example, if drivers disregard the temporary stop sign or the speed-limit sign, DAS will notice the driver and give an emergency warning to avoid accident happen.However, it is difficult to detect RS from videos directly, due to the unknown changes of driving environment, such as lighting, size, and rotation. The lighting problem is existed in natural environments and cannot be avoided. Usually, it is various dues to the weather changes (rainy, cloudy, smog, haze, sunny, etc.), time of day changes (morning, noon and night etc.) and differences of RS itself (paint color, fade etc.). It affects the purity of color and intensity largely. If the lighting problem cannot be overcome, almost all kinds of methods will be hard to obtain ideal results. The size problem is usually caused by distance. Although the size of RS is standard, it is always changing during driving in visual. For this reason, if only the RS with specific size is detected, it will be useless in practice. The rotation problem also happens sometimes. Although the normal position of RS is perpendicular to the trajectory of the vehicle, but as time pass by or the angle of view, the RS may not in the original position or viewpoint. If this problem cannot be solved, some RS will be missed. Therefore, these three kinds of problem should be conquered for RS detecting in natural condition. For the RS can be detected and used in DAS, many researchers have been devoted to solve these problems [1]. In recent years, the color-based approach becomes popular, because it is one major feature and many methods can...

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Section: Improved Cbm In Ccs and Its Separationmentioning

confidence: 98%

Section: Original Color Barycenter Model and Its Significantmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved Color Barycenter Model and Its Separation for Road Sign Detection

Zhang

Kamata

2013

IEICE Trans. Inf. & Syst.

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GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

Yang

Tang

et al. 2020

IEEE Access

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Polar Harmonic Transform (PHT) is termed to represent a set of transforms those kernels are basic waves and harmonic in nature, which can improve the effect in Intelligent Transportation System (ITS) applications. PHTs consist of Polar Complex Exponential Transform (PCET), Polar Cosine Transform (PCT) and Polar Sine Transform (PST). PHTs can extract orthogonal and rotation invariant features and demonstrated superior performance in various image processing and computer vision applications. For real time systems and large multimedia databases, execution efficiency is always a significant challenge. With widespread use of Graphics Processing Unit (GPU), this study presents GPU based PHTs. Proposed methods are based on mathematical properties of PHTs and optimization techniques of GPU. Optimal parameter selections for GPU execution are also discussed. In our experiments, proposed methods are over 1800 times faster. INDEX TERMS GPU, Polar harmonic transform, feature extraction, intelligent transportation system.

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A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

2020

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Researches on machine vision-based driver fatigue detection algorithm have improved traffic safety significantly. Generally, many algorithms do not analyze driving state from driver characteristics. It results in some inaccuracy. The paper proposes a fatigue driving detection algorithm based on facial multifeature fusion combining driver characteristics. First, we introduce an improved YOLOv3-tiny convolutional neural network to capture the facial regions under complex driving conditions, eliminating the inaccuracy and affections caused by artificial feature extraction. Second, on the basis of the Dlib toolkit, we introduce the Eye Feature Vector(EFV) and Mouth Feature Vector(MFV), which are the evaluation parameters of the driver's eye state and mouth state, respectively. Then, the driver identity information library is constructed by offline training, including driver eye state classifier library, driver mouth state classifier library, and driver biometric library. Finally, we construct the driver identity verification model and the driver fatigue assessment model by online assessment. After passing the identity verification, calculate the driver's closed eyes time, blink frequency and yawn frequency to evaluate the driver's fatigue state. In simulated driving applications, our algorithm detects the fatigue state at a speed of over 20fps with an accuracy of 95.10%.

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A Novel Color Descriptor for Road-Sign Detection

Cited by 6 publications

References 19 publications

Improved Color Barycenter Model and Its Separation for Road Sign Detection

Improved Color Barycenter Model and Its Separation for Road Sign Detection

GPU Accelerated Polar Harmonic Transforms for Feature Extraction in ITS Applications

A Fatigue Driving Detection Algorithm Based on Facial Multi-Feature Fusion

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