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Triple-feature-based Particle Filter Algorithm Used in Vehicle Tracking Applications

ABDULLA, A. A., GRAOVAC, S. See more information about  GRAOVAC, S. on SCOPUS See more information about  GRAOVAC, S. on SCOPUS See more information about GRAOVAC, S. on Web of Science, PAPIC, V. See more information about  PAPIC, V. on SCOPUS See more information about  PAPIC, V. on SCOPUS See more information about PAPIC, V. on Web of Science, KOVACEVIC., B. See more information about KOVACEVIC., B. on SCOPUS See more information about KOVACEVIC., B. on SCOPUS See more information about KOVACEVIC., B. on Web of Science
 
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Download PDF pdficon (1,921 KB) | Citation | Downloads: 646 | Views: 496

Author keywords
image color analysis, image edge detection, image sequence analysis, image texture analysis, particle filters

References keywords
tracking(35), object(15), filter(11), imaging(9), electronic(8), vision(7), information(7), video(6), vehicle(6), time(6)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2021-05-31
Volume 21, Issue 2, Year 2021, On page(s): 3 - 14
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2021.02001
Web of Science Accession Number: 000657126200001
SCOPUS ID: 85107793787

Abstract
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This work is oriented toward video tracking of vehicles in a typical traffic environment, based on particle filters. The proposed tracking algorithm is based on simultaneous usage of three different image features - color, edge orientation, and texture. All three features are related to the contents of a rectangular window that includes both the vehicle that is tracked and local background and they are represented in the form of appropriate histograms. Based on individual estimates produced by every single feature, the resultant estimate is made by their weighted averaged. Weighting factors are adaptively changing depending on the quality of a particular feature, estimated by calculations of average similarities between the reference window and the set of windows on particles' positions. The tracking accuracies of single-feature and three-features-based filters have been verified using the set of traffic sequences illustrating the presence of typical disturbances (shadows, partial and full occlusions, maneuvering etc.).


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References Weight

Web of Science® Citations for all references: 11,226 TCR
SCOPUS® Citations for all references: 15,191 TCR

Web of Science® Average Citations per reference: 216 ACR
SCOPUS® Average Citations per reference: 292 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2021-11-24 12:32 in 496 seconds.




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