کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
6923636 1448362 2018 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Robust feature point detectors for car make recognition
ترجمه فارسی عنوان
آشکارسازهای نقطه ای قوی برای تشخیص خودرو
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
An Automatic Vehicle Make and Model Recognition (AVMMR) system can be a useful add-on tool to Automatic Number Plate Recognition (ANPR) to address potential car cloning, including intelligence collection by the police to outline past and recent car movement and travel patterns. Although several AVMMR systems have been proposed, the approaches perform sub-optimally under various environmental conditions, including occlusion and/or poor lighting distortions. This paper studies the effectiveness of deploying robust local feature points that can address these limitations. The proposed methods utilize a modification of two-dimensional feature points such as SIFT, SURF, etc. and their combinations. When SIFT gets combined with the multi-scale Harris/multi-scale Hessian methods, it could outperform existing approaches. Experimental evaluations using 4 different benchmark datasets are conducted to demonstrate the robustness of the proposed techniques and their abilities to detect and identify car makes and models under various environmental conditions. SIFT- DoG, SIFT- multiscale Hessian, and SIFT- multiscale Harris are shown to yield the best results for our datasets with higher recognition rates than those achieved with other existing methods in the literature. Therefore, it can then be concluded that the combination of certain covariant feature detectors and descriptors can outperform other methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers in Industry - Volume 100, September 2018, Pages 129-136
نویسندگان
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