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Open AccessArticle

Low-Rank Multi-Channel Features for Robust Visual Object Tracking

ACTSENA Research Group, Telecommunication Engineering Department, University of Engineering and Technology Taxila, Punjab 47050, Pakistan
Department of Electrical Engineering, Polytechnique Montreal, Montreal, QC H3T 1J4, Canada
Department of Electronic Systems, Royal Institute of Technology (KTH), Isafjordsgatan 26, SE 16440 Stockholm, Sweden
Department of Information Technology, TUCS, University of Turku, 20520 Turku, Finland
Authors to whom correspondence should be addressed.
Symmetry 2019, 11(9), 1155;
Received: 15 August 2019 / Revised: 5 September 2019 / Accepted: 6 September 2019 / Published: 11 September 2019
(This article belongs to the Special Issue Deep Learning-Based Biometric Technologies)
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. View Full-Text
Keywords: circulant matrix; texture; tracking; convolution; filter circulant matrix; texture; tracking; convolution; filter
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Fawad; Jamil Khan, M.; Rahman, M.; Amin, Y.; Tenhunen, H. Low-Rank Multi-Channel Features for Robust Visual Object Tracking. Symmetry 2019, 11, 1155.

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