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  • 1.
    Fawad,
    et al.
    Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan.
    Khan, Muhammad Jamil
    Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan..
    Rahman, MuhibUr
    Polytech Montreal, Dept Elect Engn, Montreal, PQ H3T 1J4, Canada..
    Amin, Yasar
    Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan..
    Tenhunen, Hannu
    KTH, School of Electrical Engineering and Computer Science (EECS), Electronics, Integrated devices and circuits. Univ Turku, Dept Informat Technol, TUCS, FIN-20520 Turku, Finland..
    Low-Rank Multi-Channel Features for Robust Visual Object Tracking2019In: Symmetry, ISSN 2073-8994, E-ISSN 2073-8994, Vol. 11, no 9, article id 1155Article in journal (Refereed)
    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.

  • 2.
    Saeed, Ayesha
    et al.
    Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
    Fawad,
    Khan, Muhammad Jamil
    Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
    Riaz, Muhammad Ali
    Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
    Shahid, Humayun
    Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
    Khan, Mansoor Shaukat
    COMSATS Univ Islamabad, Math Dept, Islamabad 45550, Pakistan..
    Amin, Yasar
    Univ Engn & Technol Taxila, Telecommun Engn Dept, ACTSENA Res Grp, Taxila 47050, Pakistan..
    Loo, Jonathan
    Univ West London, Sch Comp & Engn, London W5 5RF, England..
    Tenhunen, Hannu
    KTH, School of Electrical Engineering and Computer Science (EECS), Electronics.
    Robustness-Driven Hybrid Descriptor for Noise-Deterrent Texture Classification2019In: IEEE Access, E-ISSN 2169-3536, Vol. 7, p. 110116-110127Article in journal (Refereed)
    Abstract [en]

    A robustness-driven hybrid descriptor (RDHD) for noise-deterrent texture classification is presented in this paper. This paper offers the ability to categorize a variety of textures under challenging image acquisition conditions. An image is initially resolved into its low-frequency components by applying wavelet decomposition. The resulting low-frequency components are further processed for feature extraction using completed joint-scale local binary patterns (CJLBP). Moreover, a second feature set is obtained by computing the low order derivatives of the original sample. The evaluated feature sets are integrated to get a final feature vector representation. The texture-discriminating performance of the hybrid descriptor is analyzed using renowned datasets: Outex original, Outex extended, and KTH-TIPS. The experimental results demonstrate a stable and robust performance of the descriptor under a variety of noisy conditions. An accuracy of 95.86%, 32.52%, and 88.74% at noise variance of 0.025 is achieved for the given datasets, respectively. A comparison between performance parameters of the proposed paper with its parent descriptors and recently published paper is also presented.

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