Color exploitation in hog-based traffic sign detection

Creusen, Ivo; Wijnhoven, Rob G. J.; Herbschleb, Ernst

doi:10.1109/icip.2010.5651637

Cited by 116 publications

(40 citation statements)

References 7 publications

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“…Given the sign category, a second classification is performed. A colour-HOG feature is used in this case, which is created by concatenating the HOG feature of each colour channel of the image in the CIELab colour space (Creusen et al, 2010). This feature is again classified by means of SVM, and the type of the sign is finally retrieved.…”

Section: Point Cloud Projection On Images and Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The periodic inspection of certain infrastructure features plays a key role for road network safety and preservation, and for developing optimal maintenance planning that minimize the life-cycle cost of the inspected features. Mobile Mapping Systems (MMS) use laser scanner technology in order to collect dense and precise three-dimensional point clouds that gather both geometric and radiometric information of the road network. Furthermore, time-stamped RGB imagery that is synchronized with the MMS trajectory is also available. In this paper a methodology for the automatic detection and classification of road signs from point cloud and imagery data provided by a LYNX Mobile Mapper System is presented. First, road signs are detected in the point cloud. Subsequently, the inventory is enriched with geometrical and contextual data such as orientation or distance to the trajectory. Finally, semantic content is given to the detected road signs. As point cloud resolution is insufficient, RGB imagery is used projecting the 3D points in the corresponding images and analysing the RGB data within the bounding box defined by the projected points. The methodology was tested in urban and road environments in Spain, obtaining global recall results greater than 95%, and F-score greater than 90%. In this way, inventory data is obtained in a fast, reliable manner, and it can be applied to improve the maintenance planning of the road network, or to feed a Spatial Information System (SIS), thus, road sign information can be available to be used in a Smart City context.

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Section: Point Cloud Projection On Images and Traffic Sign Recognitionmentioning

confidence: 99%

Automatic Road Sign Inventory Using Mobile Mapping Systems

Soilán¹,

Riveiro²,

Martínez-Sánchez³

et al. 2016

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

View full text Add to dashboard Cite

show abstract

“…The CIELab and YCbCr color spaces were used for different shapes of traffic signs by [32]. In particular, they have found that YCbCr space is suitable for segmentation of triangular signs.…”

Section: Ycbcr Color Spacementioning

confidence: 99%

Detection and recognition of traffic signs inside the attentional visual field of drivers

Zabihi

Beauchemin

et al. 2017

2017 IEEE Intelligent Vehicles Symposium (IV)

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Traffic sign detection and recognition systems are essential components of Advanced Driver Assistance Systems and self-driving vehicles. In this contribution we present a vision-based framework which detects and recognizes traffic signs inside the attentional visual field of drivers. This technique takes advantage of the driver's 3D absolute gaze point obtained through the combined use of a front-view stereo imaging system and a non-contact 3D gaze tracker. We used a linear Support Vector Machine as a classifier and a Histogram of Oriented Gradient as features for detection. Recognition is performed by using Scale Invariant Feature Transforms and color information. Our technique detects and recognizes signs which are in the field of view of the driver and also provides indication when one or more signs have been missed by the driver.

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“…The fog lamps type selection and calculation method were established by Yang [5] from the fog lamps longitudinal and transverse location and installation height, the fog lamps engineering design theory, in view of the fog lamp brightness and diameter. Much research has been done both at home and abroad from the fog area road traffic safety guidance [6][7][8], however, these researches focused more from the viewpoint of security equipment and modern network technologies for building guidance systems, rather than from the perspective of landscape color or design to improve road traffic safety in foggy areas.…”

Section: The Civil Engineering Journal 3-2017 -----------------------mentioning

confidence: 99%

Analysis on the Influence of Accumulation Effect of Landscape Color on Traffic Safety in the Foggy Sections of Expressways

Li¹,

Tang²

2017

CEJ

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The landscape color of expressways has a significant impact on a driver's visual response, thus affecting the incidence of expressways traffic accidents. Although this problem has been analyzed and discussed from different angles, there is rare quantitative analysis on the influence of accumulation effect of landscape color on the traffic safety in foggy sections of expressways. In this paper, the color combination and stroboflash of fog lamps on both sides of the road were designed from the perspective of the color research of road landscapes, and the cumulative effect of colors and stroboflash of fog lamps on the drivers' mind was also studied. Then the interactive cumulative model based on landscape color and driving time in heavy-fog environment was established. Finally, multi-group of fog-lamp colors and spacing experiment program allowing a visibility of 200-500m in fog environments was designed by using Tobii eye-movement instrument and the UC-win/Road simulation software. According to the program, simulations were performed on several drivers to test the influence of road-landscape colors in foggy environments on the drivers' visual psychology. The results of the simulation experiment showed that the colors and stroboflash of fog lamps on both sides of the road have a positive effect on the drivers' safety when the visibility range is 200-500m, and the driving speed is less than 70 km/h, in addition, the red and yellow were found to have the largest influence among all the studied colors. These results show that it is necessary to design landscape colors in the foggy sections of expressways.

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Color exploitation in hog-based traffic sign detection

Cited by 116 publications

References 7 publications

Automatic Road Sign Inventory Using Mobile Mapping Systems

Automatic Road Sign Inventory Using Mobile Mapping Systems

Detection and recognition of traffic signs inside the attentional visual field of drivers

Analysis on the Influence of Accumulation Effect of Landscape Color on Traffic Safety in the Foggy Sections of Expressways

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