An Approach for Vehicle's Classification Using BRISK Feature Extraction

Abstract

This study aimed to classify vehicles according to their categories, consisting of motorcycles, light vehicles, and heavy vehicles. For this purpose, there were three main techniques discussed: vehicle detection using Background Subtraction, feature extraction using Binary Robust Invariant Scalable Keypoint (BRISK), and vehicle classification using the K-Nearest Neighbors (KNN) algorithm for most cases. The dataset consisted of432 images for the training stage and one video data for the testing stage. The system performance was evaluated by reviewing the BRISK threshold value ranging from 10 to 80 with a k-value on KNN of 6. Results showed that the highest F1 scores were 96%, 86%, and 67% for motorcycles, light vehicles, and heavy vehicles, consecutively.

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