View-independent Vehicle Category Classification System
| العنوان: | View-independent Vehicle Category Classification System |
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| المؤلفون: | Sara Baghdadi, Noureddine Aboutabit |
| المصدر: | International Journal of Advanced Computer Science and Applications. 12 |
| بيانات النشر: | The Science and Information Organization, 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | General Computer Science, Local binary patterns, business.industry, Computer science, Deep learning, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Pattern recognition, Convolutional neural network, Vehicle category, Support vector machine, Task (computing), ComputingMethodologies_PATTERNRECOGNITION, Gabor filter, Histogram, Artificial intelligence, business |
| الوصف: | Vehicle category classification is important, but it is a challenging task, especially, when the vehicles are captured from a surveillance camera with different view angles. This paper aims to develop a view-independent vehicle category classification system. It proposes a two-phase system: one phase recognizes the view angles helping the second phase to recognize the vehicle category including bus, car, motorcycle, and truck. In each phase, several descriptors and Machine Learning techniques including traditional algorithms and Deep neural networks are employed. In particular, we used three descriptors: HOG (Histogram of Oriented Gradient), LBP (Local Binary Patterns) and Gabor filter with two classifiers SVM (Support Vector Machine) and k-NN (k-Nearest Neighbor). And also, we used the Convolutional Neural Network (CNN, or ConvNet). Three experiments have been elaborated based on many datasets. The first experiment is dedicated to choosing the best approach for the recognition of views: rear or front. The second experiment aims to classify the vehicle categories based on each view. In the third experiment, we developed the overall system, the categories were classified independently of the view. Experimental results reveal that CNN gives the highest recognition accuracy of 94.29% in the first experiment, and HOG with SVM or k-NN gives the best results (99.58%, 99.17%) in the second experiment. The system can robustly recognize vehicle categories with an accuracy of 95.77%. |
| تدمد: | 2156-5570 2158-107X |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::55fd93681fea7e6f1f223a93fb28eb48 https://doi.org/10.14569/ijacsa.2021.0120786 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi...........55fd93681fea7e6f1f223a93fb28eb48 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 21565570 2158107X |
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