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Low-rank multi-channel features for robust visual object tracking

Muhammad Jamil Khan, MuhibUr Rahman, Yasar Amin and Hannu Tenhunen

Article (2019)

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Cite this document: Jamil Khan, M., Rahman, M.U., Amin, Y. & Tenhunen, H. (2019). Low-rank multi-channel features for robust visual object tracking. Symmetry, 11(9). doi:10.3390/sym11091155
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Abstract

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.

Uncontrolled Keywords

circulant matrix; texture; tracking; convolution; filter

Open Access document in PolyPublie
Subjects: 2500 Génie électrique et électronique > 2500 Génie électrique et électronique
Department: Département de génie électrique
Research Center: Non applicable
Funders: Higher Education Commission (HEC) of Pakistan - Technology Development Fund, ASR&TD-UETT faculty research grant
Grant number: TDF-67/2017
Date Deposited: 16 Aug 2021 13:18
Last Modified: 03 Jun 2022 15:34
PolyPublie URL: https://publications.polymtl.ca/4865/
Document issued by the official publisher
Journal Title: Symmetry (vol. 11, no. 9)
Publisher: MDPI
Official URL: https://doi.org/10.3390/sym11091155

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