کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4970076 | 1450025 | 2017 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Local texture patterns for traffic sign recognition using higher order spectra
ترجمه فارسی عنوان
الگوهای بافت محلی برای تشخیص نشانه های ترافیک با استفاده از طیف های مرتبه بالاتر
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
آنتروپی، تعبیه گراف، طیف ترتیب بالاتر، سیستم حمل و نقل هوشمند شناسایی ترافیک،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Traffic sign recognition (TSR) is considered as one of the most important modules of driver assistance system (DAS). It can be used as a decision supporting tool for driver and autonomous vehicles. Eventually, TSR is a large-scale feature learning problem and hence attracted the attention of researchers recently. The essential parameters such as huge training dataset size, recognition accuracy and computational complexity need to be considered while designing a practical TSR system. In this paper, we have used higher order spectra (HOS) coupled with texture based features to develop an efficient TSR model. These features represent the shape and content of the traffic signs clearly. Then a subspace learning method with graph embedding under linear discriminant analysis framework is used to increase the discrimination power between various traffic symbols. As a result the proposed method attained a maximum recognition accuracy of 98.89%. The proposed method is evaluated using two publicly available datasets such as, Belgium traffic sign classification (BTSC) and German traffic sign recognition benchmark (GTSRB). Our experimental results demonstrate that the proposed approach is computationally efficient and shows promising recognition accuracy.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition Letters - Volume 94, 15 July 2017, Pages 202-210
Journal: Pattern Recognition Letters - Volume 94, 15 July 2017, Pages 202-210
نویسندگان
Anjan Gudigar, Shreesha Chokkadi, U. Raghavendra, U. Rajendra Acharya,