Melola Discovery

← Melola Discovery

Akses terbuka Jurnal SINTA 1

Vehicle detection system based on shape, color, and time-motion

IAES International Journal of Artificial Intelligence · Vol. 12 No. 3 (2023) · hlm. 1070–1082

DOI
10.11591/ijai.v12.i3.pp1070-1082
ISSN
2252-8938, 2089-4872
Terbit
2023-09-01
Penerbit
Institute of Advanced Engineering and Science
Bahasa
eng
Lisensi
creativecommons.org/licenses/by-sa/4.0

Abstrak

Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.

Kata kunci

Jaringan kolaborasi para penulis

40 penulis · 96 tautan kepenulisan bersama · 51 lintas organisasi · menampilkan 40 yang paling banyak terhubung

Arahkan kursor atau ketuk sebuah nama untuk menyorot rekan penulisnya. Klik dua kali untuk membuka halaman penulis.

  • Universitas Sumatera Utara
  • Politeknik Negeri Medan
  • Binus University
  • Universitas Raharja
  • Medan Area University
  • Nasional University
  • Universitas Muhammadiyah Sumatera Utara
  • Organisasi lain
  • Afiliasi tidak diketahui
  • Organisasi berbeda
  • Organisasi sama
  • Afiliasi tidak diketahui
  • Garis lebih tebal = lebih banyak artikel bersama · lingkaran lebih besar = lebih banyak artikel
Tampilkan sebagai tabel
Rekan penulisOrganisasiArtikel bersama
Herman MawengkangUniversitas Sumatera Utara4
Al-Khowarizmi Al-KhowarizmiUniversitas Muhammadiyah Sumatera Utara3
Sumarlin SumarlinUniversitas Sumatera Utara3
Dadang PriyantoUniversitas Bumigora2
Mochamad WahyudiUniversitas Bina Sarana Informatika2
Hengki Tamando SihotangAfiliasi tidak diketahui2
Desi VinsensiaAfiliasi tidak diketahui2
Poltak SihombingUniversitas Sumatera Utara2
Marwan M. Al-AkaidiArab Open University1
Edi AbdurachmanBinus University1
Arief RamadhanTelkom University1
Gunadi EmmanuelBinus University1
Agung TrisetyarsoBinus University1
Erna Budhiarti NababanUniversitas Sumatera Utara1
Heru MardiansyahUniversitas Sumatera Utara1
Saib SuwiloUniversitas Sumatera Utara1
Naemah MubarakahUniversitas Sumatera Utara1
Fahmi FahmiUniversitas Sumatera Utara1
Eka Purnama HarahapUniversitas Raharja1
Lina Naelal HusnaUniversitas Raharja1
ZulhamMedan Area University1
Zulkarnain LubisMedan Area University1
Muhammad Reza AuliaUniversitas Teuku Umar1
Sherly AstutiSun Yat-sen University1
Muhammad SalamuddinUniversitas Raharja1
Surya Hadi DharmaPoliteknik Negeri Medan1
Ilmi AbdullahUniversitas Sumatera Utara1
Muhammad SabriUniversitas Sumatera Utara1
Muhammad TurmuziUniversitas Sumatera Utara1
Arridina Susan SilitongaPoliteknik Negeri Medan1
Marnida YusfianiState University of Medan1
Ucuk DarusalamNasional University1
Purnomo Sidi PriambodoUniversity of Indonesia1
Novi Dian NathasiaNasional University1
Gunawan GunawanUniversitas Halu Oleo1
Sutarman SutarmanUniversitas Sumatera Utara1

Afiliasi berasal dari OpenAlex; metadata OAI-PMH jurnal tidak memuatnya. Penulis tanpa ID OpenAlex dicocokkan berdasarkan nama, sehingga dua orang bisa tergabung atau satu orang bisa terpisah.

Sumber metadata: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, pertama di-harvest 8 Nov 2026, terakhir terlihat 8 Nov 2026.
Metadata asli (oai_dc) 
<oai_dc:dc xmlns="http://www.openarchives.org/OAI/2.0/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/  http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
	<dc:title xml:lang="en-US">Vehicle detection system based on shape, color, and time-motion</dc:title>
	<dc:creator>Amelia, Afritha</dc:creator>
	<dc:creator>Zarlis, Muhammad</dc:creator>
	<dc:creator>Suherman, Suherman</dc:creator>
	<dc:creator>Efendi, Syahril</dc:creator>
	<dc:subject xml:lang="en-US"></dc:subject>
	<dc:subject xml:lang="en-US">Disparity; Image depth; Path error; Phase detection auto focus; Vehicle detection and tracking</dc:subject>
	<dc:description xml:lang="en-US">Vehicle detection application can assist in-vehicle surveillance functions and have implications for various fields. A vehicle can be identified through the license number attached to its license plate, the color and its shape. Vehicle detection can make use of multimedia sensors so that the design and detection performances can be optimal. Sensor performances are influenced by factors such as the number of multimedia sensors, sensor placement, sensor positioning, and schemes in case of system failure. This study makes use of multimedia sensors with cameras equipped by a phase detection auto focus (PDAF) technology which is like a pair of eyes to see an object. This study analyses 134 vehicles with number detection and various colors to see the effect on the detection and recognition processes. The cars were passed through the camera 10 times at a speed of 10-15 km/hour with various camera distances and positions. Various values and depths of the images were generated. The farther the distance the higher the disparity values. For maximum distance of 50 m, disparity is 6.20×106 and image depth is 16.88×109. Vehicle color influences detection with orange has the best accuracy, but the gray has the largest path error value.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US"></dc:contributor>
	<dc:date>2023-09-01</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://ijai.iaescore.com/index.php/IJAI/article/view/21661</dc:identifier>
	<dc:identifier>10.11591/ijai.v12.i3.pp1070-1082</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 12, No 3: September 2023; 1070-1082</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v12.i3</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/21661/13674</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
</oai_dc:dc>