Reducing Area Recognition for Vehicle Model Classification using Car's Front Side

A Car Make and Model Recognition (CMMR) system plays an essential role in Intelligent Transport System (ITS) development. The challenge is identifying the features of a car and simplifying the process of a system. This work presents a system that can handle the challenges. This research aims to classify car models based on global features in the car's frontside view image. The dataset used consists of 5 classes spread into 387 images with 312 train data and 75 test data. The method used in feature extraction is the Bag of ORB Feature (BOF) method, which is a combination of the Oriented and Rotated BRIEF (ORB) feature extraction method and the Bag of Visual Word (BOVW) concept. While at the classification stage, it uses the Support Vector Machine (SVM) method. The results show that the proposed approach can overcome the challenges of CMMR with an F1 score for each class of 96.3%, 91.2%, 87.0%, 81.8%, and 85.7%. In addition, the approach of using the car's front-side view image can also increase the system performance with an average increase of 10% than using the whole car image.