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MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING

Jurnal Ilmu Komputer dan Informasi (JIKI) · Vol. 11 No. 1 (2018) · pp. 17–26

DOI
10.21609/jiki.v11i1.581
ISSN
2502-9274, 2088-7051
Published
2018-02-28
Publisher
Faculty of Computer Science - Universitas Indonesia
Language
eng

Abstract

Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.

Keywords

Collaboration network of the authors

22 authors · 52 co-authorship links · 22 across different organisations

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

  • University of Indonesia
  • The University of Melbourne
  • Pelita Harapan University
  • University of Edinburgh
  • University of Auckland
  • Monash University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Dewa Made Sri ArsaUniversity of Indonesia3
Fikrul Arif NadraUniversity of Indonesia1
Heri KurniawanUniversity of Indonesia1
Benny HardjonoPelita Harapan University1
Novian HabibieUniversity of Indonesia1
Machmud Roby AlhamidiUniversity of Indonesia1
Ari WibisonoUniversity of Indonesia1
Dwi PurnomoAffiliation unknown1
Muhammad Febrian RachmadiUniversity of Edinburgh1
Agung SantosoUniversity of Indonesia1
Rafli FilanoUniversity of Indonesia1
Nurul HanifahUniversity of Indonesia1
Aniati Murni ArymurthyUniversity of Indonesia1
Sesa WigunaUniversity of Auckland1
Wisnu JatmikoUniversity of Indonesia1
Siti Hadiyan PratiwiUniversity of Indonesia1
Putri ShaniyaUniversity of Indonesia1
Bayu AnggorojatiMonash University1
Haidlir Achmad NaqviUniversity of Indonesia1

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://jiki.cs.ui.ac.id/index.php/jiki/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">MULTI OBJECT DETECTION AND TRACKING USING OPTICAL FLOW DENSITY – HUNGARIAN KALMAN FILTER (OFD - HKF) ALGORITHM FOR VEHICLE COUNTING</dc:title>
	<dc:creator>Soleh, Muhamad</dc:creator>
	<dc:creator>Jati, Grafika</dc:creator>
	<dc:creator>Hilman, Muhammad Hafizhuddin</dc:creator>
	<dc:subject xml:lang="en-US">Intelligent Transportation Systems</dc:subject>
	<dc:subject xml:lang="en-US">Optical Flow Density</dc:subject>
	<dc:subject xml:lang="en-US">Hungarian Kalman Filter</dc:subject>
	<dc:subject xml:lang="en-US">Single Line Counting</dc:subject>
	<dc:description xml:lang="en-US">Intelligent Transportation Systems (ITS) is one of the most developing research topic along with growing advance technology and digital information. The benefits of research topic on ITS are to address some problems related to traffic conditions. Vehicle detection and tracking is one of the main step to realize the benefits of ITS. There are several problems related to vehicles detection and tracking. The appearance of shadow, illumination change, challenging weather, motion blur and dynamic background such a big challenges issue in vehicles detection and tracking. Vehicles detection in this paper using the Optical Flow Density algorithm by utilizing the gradient of object displacement on video frames. Gradient image feature and HSV color space on Optical flow density guarantee the object detection in illumination change and challenging weather for more robust accuracy. Hungarian Kalman filter algorithm used for vehicle tracking. Vehicle tracking used to solve miss detection problems caused by motion blur and dynamic background. Hungarian kalman filter combine the recursive state estimation and optimal solution assignment. The future positon estimation makes the vehicles detected although miss detection occurance on vehicles. Vehicles counting used single line counting after the vehicles pass that line. The average accuracy for each process of vehicles detection, tracking, and counting were 93.6%, 88.2% and 88.2% respectively.</dc:description>
	<dc:publisher xml:lang="en-US">Faculty of Computer Science  - Universitas Indonesia</dc:publisher>
	<dc:date>2018-02-28</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/581</dc:identifier>
	<dc:identifier>10.21609/jiki.v11i1.581</dc:identifier>
	<dc:source xml:lang="en-US">Jurnal Ilmu Komputer dan Informasi; Vol. 11 No. 1 (2018): Jurnal Ilmu Komputer dan Informasi (Journal of Computer Science and Information); 17-26</dc:source>
	<dc:source>2502-9274</dc:source>
	<dc:source>2088-7051</dc:source>
	<dc:source>10.21609/jiki.v11i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://jiki.cs.ui.ac.id/index.php/jiki/article/view/581/389</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2018 Jurnal Ilmu Komputer dan Informasi</dc:rights>
</oai_dc:dc>