# An Approach for Vehicle's Classification Using BRISK Feature Extraction
> Kurniawan Amiruddin M.R.

URL kanonis: https://discover.unhas.ac.id/publications/an-approach-for-vehicles-classification-using-brisk-feature-extraction
Jurnal / Konferensi: Proceeding Icera 2021 2021 3rd International Conference on Electronics Representation and Algorithm
Tahun terbit: 2021
DOI: https://doi.org/10.1109/ICERA53111.2021.9538701
Citations: 8

## Authors
- Kurniawan Amiruddin M.R.

## 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.

## Keywords
- Background subtraction
- Feature extraction
- Artificial intelligence
- Computer science
- Pattern recognition (psychology)
- Threshold limit value
- Computer vision
- Pixel
- Environmental health
- Medicine

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Sumber: Discover Unhas — RIMS Universitas Hasanuddin.
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