کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
382762 660788 2013 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Design of a visual perception model with edge-adaptive Gabor filter and support vector machine for traffic sign detection
چکیده انگلیسی

Traffic sign detection is a useful application for driving assistance systems, and it is necessary to accurately detect traffic signs before they can be identified. Sometimes, however, it is difficult to detect traffic sign, which may be obscured by other objects or affected by illumination or lightning reflections. Most previous work on this topic has been based on region of interest analysis using the color information of traffic signs. Although this provides a simple way to segment signs, this approach is weak when a sign is affected by illumination or its own color information is distorted. To overcome this, this paper introduces a robust traffic detection framework for cluttered scenes or complex city views that does not use color information. Moreover, the proposed method can detect traffic sign in the night. We establish an edge-adaptive Gabor function, which is derived from human visual perception. It is an enhanced version of the original Gabor filter, and filters out unnecessary information to provide robust recognition. It decomposes the directional information of objects and reflects specific shapes of traffic signs. Once the extracted feature is obtained, a support vector machine detects the traffic sign. Applying scale-space theory, it is possible to resolve the scaling problem of the objects that we want to find. Our system shows robust performance in traffic sign detection, and experiments on real-world scenes confirmed its properties.


► This paper models a traffic sign detection system robust to illumination effects.
► We propose a novel feature extraction with scale-space theory.
► We use a single SVM and multiple SVM classifiers for accurate detection.
► Real-world scene images with traffic signs were used for evaluation.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Expert Systems with Applications - Volume 40, Issue 9, July 2013, Pages 3679–3687
نویسندگان
, ,