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HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway

IJCCS (Indonesian Journal of Computing and Cybernetics Systems) · Vol. 14 No. 3 (2020) · pp. 231–242

DOI
10.22146/ijccs.54050
ISSN
2460-7258, 1978-1520
Published
2020-07-31
Publisher
IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

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.

Keywords

Collaboration network of the authors

40 authors · 71 co-authorship links · showing the 40 most connected

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • University of Brawijaya
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Yusuf Priyo AnggodoUniversity of Brawijaya3
Fitra Abdurrachman BachtiarUniversity of Brawijaya3
Asyrofa RahmiUniversity of Brawijaya2
Syaiful AnamUniversity of Brawijaya2
Adyan Nur AlfiyatinUniversity of Brawijaya2
Hurriyatul FitriyahUniversity of Brawijaya2
Rizal MaulanaUniversity of Brawijaya2
Randy Cahya WihandikaUniversity of Brawijaya2
Muhammad Ali FauziUniversity of Brawijaya2
Dahnial SyauqyUniversity of Brawijaya2
Ida WahyuniUniversity of Brawijaya1
Ervin YohannesUniversity of Brawijaya1
Hilman Nuril HadiUniversity of Brawijaya1
Ihwanudien Hasan RobbaniUniversity of Brawijaya1
Enny TrisnawatiUniversity of Brawijaya1
Rakhmadina NoviyantiUniversity of Brawijaya1
Afrizal RivaldiUniversity of Brawijaya1
Rizal Setya PerdanaUniversity of Brawijaya1
Ghenniy RachmansyahUniversity of Brawijaya1
Ria FebriyanaUniversity of Brawijaya1
Vitara Nindya Putri HasanUniversity of Brawijaya1
Mohammad Zoqi SarwaniUniversity of Brawijaya1
Luthfi HidayatUniversity of Brawijaya1
Rifki Setya ArmandaUniversity of Brawijaya1
Garsinia Ely RianiUniversity of Brawijaya1
Fita Lathifatul Mu’asyarohAffiliation unknown1
Agus Wahyu WidodoUniversity of Brawijaya1
Diva KurnianingtyasUniversity of Brawijaya1
Muchammad Chandra Cahyo UtomoUniversity of Brawijaya1
Waskitha WijayaUniversity of Brawijaya1
Herman TolleUniversity of Brawijaya1
Candra Fajri AnandaUniversity of Brawijaya1
Farhanna Mar’iUniversity of Brawijaya1
Cleoputri YusainyUniversity of Brawijaya1
Ovy RochmawantiUniversity of Brawijaya1
Aisyah AwalinaUniversity of Brawijaya1
Qoirul KotimahUniversity of Brawijaya1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) https://journal.ugm.ac.id/ijccs/oai, first harvested 26 Okt 2026, last seen 26 Okt 2026.
Original metadata (oai_dc)
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	<dc:title xml:lang="en-US">HOG Feature Extraction and KNN Classification for Detecting Vehicle in The Highway</dc:title>
	<dc:creator>Achyunda Putra, Firnanda Al Islama</dc:creator>
	<dc:creator>Utaminingrum, Fitri</dc:creator>
	<dc:creator>Mahmudy, Wayan Firdaus</dc:creator>
	<dc:subject xml:lang="en-US">Computer Science</dc:subject>
	<dc:subject xml:lang="en-US">Histogram of Oriented Gradient (HOG); K-Nearest Neighbour (KNN); Vehicle Detection</dc:subject>
	<dc:description xml:lang="en-US">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.</dc:description>
	<dc:publisher xml:lang="en-US">IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.</dc:publisher>
	<dc:contributor xml:lang="en-US">Firnanda Al Islama A, Faculty of Computer Science, Brawijaya University</dc:contributor>
	<dc:contributor xml:lang="en-US">Fitri Utaminingrum, Faculty of Computer Science, Brawijaya University</dc:contributor>
	<dc:contributor xml:lang="en-US">Wayan Firdaus Mahmudy, Faculty of Computer Science, Brawijaya University</dc:contributor>
	<dc:date>2020-07-31</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
	<dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
	<dc:type xml:lang="en-US"></dc:type>
	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://journal.ugm.ac.id/ijccs/article/view/54050</dc:identifier>
	<dc:identifier>10.22146/ijccs.54050</dc:identifier>
	<dc:source xml:lang="en-US">IJCCS (Indonesian Journal of Computing and Cybernetics Systems); Vol 14, No 3 (2020): July; 231-242</dc:source>
	<dc:source>2460-7258</dc:source>
	<dc:source>1978-1520</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://journal.ugm.ac.id/ijccs/article/view/54050/28348</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2020 IJCCS (Indonesian Journal of Computing and Cybernetics Systems)</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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