Color transformation for improved traffic sign detection

Creusen, Ivo; Hazelhoff, Lykele

doi:10.1109/icip.2012.6466896

Cited by 12 publications

(3 citation statements)

References 13 publications

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“…At present, there are many methods for traffic sign identification. Due to the unique shape and color of traffic signs, some scholars have proposed color space [2][3] , a method [4] of identifying traffic signs according to shape characteristics, or [5] of combining color and shape characteristics, or extracting candidate regions under color space to support vector machine (SVM) classifier identification [6][7] .In recent years, due to the rise of the convolutional neural network [8] , some scholars have proposed the method of detecting traffic signs based on the convolutional neural network [9] , which extracts the candidate regions through the pre-selected box and is sent to the classification network for identification.But these methods all have some drawbacks,According to the existing publicly available traffic sign dataset, we found that the world's mainstream traffic signs, whether German traffic sign data set (GTSDB),Belgian traffic sign data set (Belgium TS), or Chinese traffic sign dataset (CTSDB), are single and fixed, warning signs are circular shape of red outer circle, and blue background circular shape. Therefore, according to the color and shape characteristics of circular traffic signs, this paper proposes an algorithm that separates red and blue color channels under HSV color space, uses Hough transformation to extract the original image candidates under each channel, and sends it to the constructed shallow convolutional neural network (CNN) classifier to detect circular traffic signs.…”

Section: Introductionmentioning

confidence: 99%

Round Traffic Sign Detection Algorithm

Huang¹,

Wang

Yuan³

2022

J. Phys.: Conf. Ser.

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Traffic signs are an important part of autonomous driving and intelligent transportation. It provides instructions for pedestrians and vehicles and is critical to road traffic safety. However, existing detection algorithms cannot achieve real-time high-precision detection. Therefore, this paper proposes an algorithm that combines traditional methods with deep learning to detect circular traffic signs. Based on the HSV color space, the red and blue channel images are separated, and the candidate regions of the original image are extracted using the Hough transform. The shallow convolutional neural network (CNN) classifier rejects not traffic signs and classifies traffic signs. Experiments show that the algorithm is real and effective. On the CPU platform, the average accuracy rate is 96.2%, and the detection speed reaches 0.3 s / frame. Under the condition of ensuring the average accuracy rate, the detection speed is greatly reduced. The algorithm achieves the fastest speed, which makes real-time high-precision detection possible. The algorithm is more suitable for vehicle embedded systems.

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Section: Introductionmentioning

confidence: 99%

Round Traffic Sign Detection Algorithm

Huang¹,

Wang

Yuan³

2022

J. Phys.: Conf. Ser.

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“…Traffic scene perception can be divided into object detection and road and lane detection. Object detection is mainly used to identify dynamic objects or traffic signs in traffic scenes, including pedestrian detection [1], vehicle detection [2] and traffic light/signal detection [3].Road and lane detection is mainly used for drivable region planning which avoids the deviation of the vehicle from the road. It mainly includes road marking detection [4], lane detection [5] and driving region detection [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Real-time Drivable Region Planning Based on 3D LiDAR

Wang¹,

Zeng²,

Xu³

et al. 2019

dtcse

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Stable, real-time, drivable area planning in a dynamic environment is an essential feature of autonomous vehicles. This paper presents an efficient 3D laser radar-based drivable area planning algorithm. In order to extract the drivable area, the original point cloud data is first downsampled to obtain a relatively sparse point cloud to reduce the complexity. Then, based on the geometric features of the pavement point and dividing the road plane, the obstacle point after the road plane is divided. The cloud is transformed into a 2D aerial view, and a series of expansion, convolution and other regional operations are performed on the bird's-eye view, and the road edge points are extracted, and the curve fitting is performed based on the least squares method to plan the drivable region. The algorithm proposed in this paper was tested on the KITTI dataset and obtained robust, high-efficiency experimental results.

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“…21 Furthermore, we have developed a custom color transformation to further improve the detection performance as described in Ref. 23. For each pixel, this method calculates the distance in color space to a set of reference colors, as specified by…”

Section: Traffic Sign and License Plate Detectionmentioning

confidence: 99%

ViCoMo: visual context modeling for scene understanding in video surveillance

et al. 2013

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Document VersionPublisher's PDF, also known as Version of Record (includes final page, issue and volume numbers) Please check the document version of this publication:• A submitted manuscript is the author's version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.• You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal ? Take down policyIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Abstract. The use of contextual information can significantly aid scene understanding of surveillance video. Just detecting people and tracking them does not provide sufficient information to detect situations that require operator attention. We propose a proof-of-concept system that uses several sources of contextual information to improve scene understanding in surveillance video. The focus is on two scenarios that represent common video surveillance situations, parking lot surveillance and crowd monitoring. In the first scenario, a pan-tiltzoom (PTZ) camera tracking system is developed for parking lot surveillance. Context is provided by the traffic sign recognition system to localize regular and handicapped parking spot signs as well as license plates. The PTZ algorithm has the ability to selectively detect and track persons based on scene context. In the second scenario, a group analysis algorithm is introduced to detect groups of people. Contextual information is provided by traffic sign recognition and region labeling algorithms and exploited for behavior understanding. In both scenarios, decision engines are used to interpret and classify the output of the subsystems and if necessary raise operator alerts. We show that using context information enables the automated analysis of complicated scenarios that were previously not possible using conventional moving object classification techniques.

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Color transformation for improved traffic sign detection

Cited by 12 publications

References 13 publications

Round Traffic Sign Detection Algorithm

Round Traffic Sign Detection Algorithm

Real-time Drivable Region Planning Based on 3D LiDAR

ViCoMo: visual context modeling for scene understanding in video surveillance

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