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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer Sistem : Jurnal Ilmu-Ilmu Teknik SMARTICS Journal Jurnal Restikom : Riset Teknik Informatika dan Komputer JiTEKH (Jurnal Ilmiah Teknologi Harapan) Rambideun : Jurnal Pengabdian Kepada Masyarakat Jurnal Informatika Polinema (JIP)
Putra, Firnanda Al Islama Achyunda
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Agriculture, Biological Sciences & Forestry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Library & Information Science Mechanical Engineering Public Health Social Sciences

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Journal : IJCCS (Indonesian Journal of Computing and Cybernetics Systems)

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HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway Firnanda Al Islama Achyunda Putra; Fitri Utaminingrum; Wayan Firdaus Mahmudy
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 14, No 3 (2020): July
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.54050

Abstract

Autonomous car is a vehicle that can guide itself without human intervention. Various types of rudderless vehicles are being developed. Future systems where computers take over the art of driving. The problem is prior to being attention in an autonomous car for obtaining the high safety. Autonomous car need early warning system to avoid accidents in front of the car, especially the system can be used in the Highway location. In this paper, we propose a vision-based vehicle detection system for Autonomous car. Our detection algorithm consists of three main components: HOG feature extraction, KNN classifier, and vehicle detection. Feature extraction has been used to recognize an object such as cars. In this case, we use HOG feature extraction to detect as a car or non-car. We use the KNN algorithm to classify. KNN Classification in previous studies had quite good results. Car detected by matching about trining data with testing data. Trining data created by extract HOG feature from image 304 x 240 pixels. The system will produce a classification between car or non-car.
Co-Authors Aditya Galih Sulaksono Ahmad Rizal Khamdani Andriyan Rizki Jatmiko As’ad Shidqy Aziz Karyudi, Mochammad Daffa Putra Listanto Tri Utomo Muhamat Ilham Ardiansa Ardiansa Nurul Hidayat Pusaka, Semerdanta Rizza Muhammad Arief Tri Afirianto Utaminingrum, Fitri Wayan Firdaus Mahmudy Yosta Yoserizal, Yosta Yulia Natasya Farah Diba Arifin