Yapay Sinir Ağları, Destek Vektör Makineleri ve AdaBoost Algoritması ile Araç Sınıflandırmasının Değerlendirilmesi

Trafik yönetimi ve bilgi sistemlerinin trafik akışını doğru sağlayabilmesi için çeşitli sensörler ve kameralar kullanarak trafik hakkında bilgi edinmesi hayati önem taşımaktadır. Bu bağlamda video kameralar son yıllarda trafik gözetim ve kontrolünde yaygın ve aktif olarak kullanılmaya başlanmıştır. Bu çalışmada araçlar boyutlarına göre üç kategoriye ayrılarak sınıflandırılmıştır. Oluşturduğumuz trafik video görüntüleri üzerinde Yapay Sinir Ağları, Destek Vektör Makineleri ve Adaboost sınıflandırıcıları ile eğitim gerçekleştirilmiş ve performansları karşılaştırılmıştır.

Anahtar Kelimeler:

Araç sınıflandırma, Akıllı trafik yönetimi, Yapay Sinir Ağları, Destek Vektör Makineleri ve Adaboost, İkili imge bölge özellikleri.

Evaluation of Vehicle Classification with Artificial Neural Networks, Support Vector Machines, and AdaBoost Algorithm

It is vital for traffic management and information systems to obtain information about the traffic using various sensors and cameras in order to provide the traffic flow correctly. In this context, video cameras have been widely and actively used in traffic surveillance and control in recent years. In this study, vehicles were classified into three categories according to their sizes. Training was carried out with Artificial Neural Networks, Support Vector Machines and Adaboost classifiers on the traffic video images we created and their performances were compared.

Keywords:

Vehicle classification, Intelligent traffic management, Neural Networks, Support Vector Machines and Adaboost, Binary image region properties,

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