Fined-Grained Vehicle Classification using Similar Auto Extracted Parts

After vehicle detection and vehicle type recognition, it is vehicle make and model recognition (VMMR) that has attracted researchers attention in the last decade. This problem is known as a hard classification problem due to the large number of classes and small inner-class distance. This paper is proposed a new method for recognition of make and model of vehicles.
The proposed approach has two parts. A new part-based approach for vehicle make and model recognition and a new method for auto extraction of parts. This approach concentrates on meaningful parts of vehicle like lights, grilles and logo for classification of different classes. The Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM) have been used for feature extraction and classification tasks respectively. For evaluation purposes, a dataset including 720 images from frontal and rear view of21 different classes of vehicles have been prepared and fully annotated based on their parts. The experimental results showed the effectiveness of the part-based approach in compare to the traditional approaches and the high accuracy gained from auto extracted parts. The proposed method achieved 100% accuracy on both frontal and rear view.