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Low-Rank Multi-Channel Features for Robust Visual Object Tracking
Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan.
Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan..ORCID-id: 0000-0001-8616-3959
Polytech Montreal, Dept Elect Engn, Montreal, PQ H3T 1J4, Canada..ORCID-id: 0000-0002-5084-7862
Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan..ORCID-id: 0000-0003-4968-993X
Vise andre og tillknytning
2019 (engelsk)Inngår i: Symmetry, ISSN 2073-8994, E-ISSN 2073-8994, Vol. 11, nr 9, artikkel-id 1155Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Kernel correlation filters (KCF) demonstrate significant potential in visual object tracking by employing robust descriptors. Proper selection of color and texture features can provide robustness against appearance variations. However, the use of multiple descriptors would lead to a considerable feature dimension. In this paper, we propose a novel low-rank descriptor, that provides better precision and success rate in comparison to state-of-the-art trackers. We accomplished this by concatenating the magnitude component of the Overlapped Multi-oriented Tri-scale Local Binary Pattern (OMTLBP), Robustness-Driven Hybrid Descriptor (RDHD), Histogram of Oriented Gradients (HoG), and Color Naming (CN) features. We reduced the rank of our proposed multi-channel feature to diminish the computational complexity. We formulated the Support Vector Machine (SVM) model by utilizing the circulant matrix of our proposed feature vector in the kernel correlation filter. The use of discrete Fourier transform in the iterative learning of SVM reduced the computational complexity of our proposed visual tracking algorithm. Extensive experimental results on Visual Tracker Benchmark dataset show better accuracy in comparison to other state-of-the-art trackers.

sted, utgiver, år, opplag, sider
MDPI , 2019. Vol. 11, nr 9, artikkel-id 1155
Emneord [en]
circulant matrix, texture, tracking, convolution, filter
HSV kategori
Identifikatorer
URN: urn:nbn:se:kth:diva-262987DOI: 10.3390/sym11091155ISI: 000489177900091OAI: oai:DiVA.org:kth-262987DiVA, id: diva2:1366997
Merknad

QC 20191031

Tilgjengelig fra: 2019-10-31 Laget: 2019-10-31 Sist oppdatert: 2019-10-31bibliografisk kontrollert

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Khan, Muhammad JamilRahman, MuhibUrAmin, YasarTenhunen, Hannu
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