An Approach for Vehicle’s Classification Using BRISK Feature Extraction


Rudini Kurniawan Amiruddin, - and Indra bayu, - and Intan Sari Areni, - and Anugrayani Bustamin, - An Approach for Vehicle’s Classification Using BRISK Feature Extraction. 2021 3rd International Conference on Electronics Representation and Algorithm (ICERA).

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Abstract (Abstrak)

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 of 432 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.

Item Type: Article
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Depositing User: - Andi Anna
Date Deposited: 07 Feb 2022 03:16
Last Modified: 22 Feb 2022 06:11
URI: http://repository.unhas.ac.id:443/id/eprint/13089

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