Traffic sign recognition using Scale Invariant Feature Transform and color classification

Kuş, Merve Can; Gokmen, M.; Etaner-Uyar, A. Şima

doi:10.1109/iscis.2008.4717875

Cited by 27 publications

(10 citation statements)

References 18 publications

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“…Another proposed method for traffic sign recognition uses the Scale Invariant Feature Transform (SIFT) [73]. This method finds local invariant features in a given image and matches these features to the features of images that exist in the training set.…”

Section: Recognition Methodsmentioning

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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Section: Recognition Methodsmentioning

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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show abstract

“…The Haar‐like features were trained with AdaBoost cascade method for sign texture representation (Hu and Tsai, 2011; Baro et al, 2009). Sign textures were also represented with Scale‐Invariant Feature Transform (SIFT) features (Tsai et al, 2010a; Kus et al, 2008). Some algorithms try to detect all sign types in the first step (Tsai et al, 2009) and recognize sign candidates in the second one (Kastner et al, 2010).…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Road Sign Inventory (IRSI) with Image Recognition and Attribute Computation from Video Log

2012

Computer aided Civil Eng

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Road signs are infrastructure assets crucial for transportation safety. Transportation agencies need to inventory signs regularly, e.g., annually or every 2 years, for asset management. This article proposes a framework of intelligent road sign inventory (IRSI) to enhance sign data collection, compared with existing manual or semiautomatic methods. The two key technical problems of sign recognition and attribute computation are addressed for IRSI. Because sign colors are important for both image segmentation and color features analysis, a novel kernel‐density‐estimation‐based statistical color model (KDE‐SCM) is proposed to describe the widespread and multimodel sign color distributions. The KDE‐SCM is embedded into a sign recognition algorithm by integrating sign shapes, textures, and other features. Furthermore, sign attributes, including the distance, local position, global positioning system (GPS) position, height, tilt angle, etc., are computed from image and GPS data. The proposed algorithms have been tested with the actual video log images collected on 35 km road segments in Kyoto City, Japan. For the KDE‐SCM color models, the false positive rates for the red and blue colors are 0.9% and 0.7%, while the false negative rates are 0.8% and 0.5%, respectively. The sign recognition algorithm was configured and applied to recognize three regulatory sign types of stop, no‐crossing, and no‐parking signs with 92.6% precision and 91.9% recall on average. The sign attribute computation model was also tested with the actual video log. The computation errors for distance, position, tilt angle, and height, etc., increase with the measurement distances. The sign‐to‐camera distances have the computation errors of less than 22 cm if the measurement distances are less than 15 m. The proposed algorithms demonstrate good potential for developing IRSI to enhance road asset data collection and processing.

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“…cale Invariant Feature Transform (SIFT), Principal Component Analysis (PCA)-SIFT and Speeded Up Robust Features (SURF) are considered to be from the most common automatic image matching methods used in photogrammetry and computer vision applications, such as image registration, camera calibration, vision based navigation [1], Simultaneous Localization And Mapping (SLAM), automatic image mosaic [2], indexing [3], recognizing panoramas [4], and traffic sign recognition [5]. These automatic image matching algorithms consist generally of two processes: feature point detection and description.…”

Section: Introductionmentioning

confidence: 99%

Speeding Up Sift, Pca-Sift and Surf Using Image Pyramid

Amami

2017

juod

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Traffic sign recognition using Scale Invariant Feature Transform and color classification

Cited by 27 publications

References 18 publications

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

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

Intelligent Road Sign Inventory (IRSI) with Image Recognition and Attribute Computation from Video Log

Speeding Up Sift, Pca-Sift and Surf Using Image Pyramid

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