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

被引:8
|
作者
Fawad [1 ]
Khan, Muhammad Jamil [1 ]
Rahman, MuhibUr [2 ]
Amin, Yasar [1 ]
Tenhunen, Hannu [3 ,4 ]
机构
[1] Univ Engn & Technol Taxila, ACTSENA Res Grp, Telecommun Engn Dept, Punjab 47050, Pakistan
[2] Polytech Montreal, Dept Elect Engn, Montreal, PQ H3T 1J4, Canada
[3] Royal Inst Technol KTH, Dept Elect Syst, Isafjordsgatan 26, SE-16440 Stockholm, Sweden
[4] Univ Turku, Dept Informat Technol, TUCS, FIN-20520 Turku, Finland
来源
SYMMETRY-BASEL | 2019年 / 11卷 / 09期
关键词
circulant matrix; texture; tracking; convolution; filter; RECOGNITION;
D O I
10.3390/sym11091155
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
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.
引用
收藏
页数:14
相关论文
共 50 条
  • [41] Infrared Target Tracking Based on Robust Low-Rank Sparse Learning
    He, Yujie
    Li, Min
    Zhang, Jinli
    Yao, Junping
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2016, 13 (02) : 232 - 236
  • [42] Robust multi-view low-rank embedding clustering
    Jian Dai
    Hong Song
    Yunzhi Luo
    Zhenwen Ren
    Jian Yang
    Neural Computing and Applications, 2023, 35 : 7877 - 7890
  • [43] Robust multi-view low-rank embedding clustering
    Dai, Jian
    Song, Hong
    Luo, Yunzhi
    Ren, Zhenwen
    Yang, Jian
    NEURAL COMPUTING & APPLICATIONS, 2023, 35 (10): : 7877 - 7890
  • [44] Dual low-rank structure embedding for robust visual information processing
    Zhou, Jianhang
    Zhang, Hengmin
    Li, Shuyi
    Zhang, Bob
    Fang, Leyuan
    Zhang, David
    KNOWLEDGE-BASED SYSTEMS, 2024, 296
  • [45] Improved Hierarchical Convolutional Features for Robust Visual Object Tracking
    Sun, Jinping
    COMPLEXITY, 2021, 2021
  • [46] Spatial and semantic convolutional features for robust visual object tracking
    Zhang, Jianming
    Jin, Xiaokang
    Sun, Juan
    Wang, Jin
    Sangaiah, Arun Kumar
    MULTIMEDIA TOOLS AND APPLICATIONS, 2020, 79 (21-22) : 15095 - 15115
  • [47] Spatial and semantic convolutional features for robust visual object tracking
    Jianming Zhang
    Xiaokang Jin
    Juan Sun
    Jin Wang
    Arun Kumar Sangaiah
    Multimedia Tools and Applications, 2020, 79 : 15095 - 15115
  • [48] Robust Neighborhood Preserving Low-Rank Sparse CNN Features for Classification
    Tang, Zemin
    Zhang, Zhao
    Ma, Xiaohu
    Qin, Jie
    Zhao, Mingbo
    ADVANCES IN MULTIMEDIA INFORMATION PROCESSING, PT I, 2018, 11164 : 357 - 369
  • [49] Robust Low-Rank and Sparse Tensor Decomposition for Low-Rank Tensor Completion
    Shi, Yuqing
    Du, Shiqiang
    Wang, Weilan
    PROCEEDINGS OF THE 33RD CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2021), 2021, : 7138 - 7143
  • [50] A Unified Bayesian Model of Time-frequency Clustering and Low-rank Approximation for Multi-channel Source Separation
    Itakura, Kousuke
    Bando, Yoshiaki
    Nakamura, Eita
    Itoyama, Katsutoshi
    Yoshii, Kazuyoshi
    2016 24TH EUROPEAN SIGNAL PROCESSING CONFERENCE (EUSIPCO), 2016, : 2280 - 2284