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

被引:6
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 62 条
[1]   Human activity recognition from 3D data: A review [J].
Aggarwal, J. K. ;
Xia, Lu .
PATTERN RECOGNITION LETTERS, 2014, 48 :70-80
[2]  
[Anonymous], 2006, P BRIT MACHINE VISIO
[3]  
[Anonymous], 2016, CVPR
[4]  
[Anonymous], 2012, PROC CVPR IEEE
[5]   Deep Learning-Based Iris Segmentation for Iris Recognition in Visible Light Environment [J].
Arsalan, Muhammad ;
Hong, Hyung Gil ;
Naqvi, Rizwan Ali ;
Lee, Min Beom ;
Kim, Min Cheol ;
Kim, Dong Seop ;
Kim, Chan Sik ;
Park, Kang Ryoung .
SYMMETRY-BASEL, 2017, 9 (11)
[6]  
Babenko B, 2009, PROC CVPR IEEE, P983, DOI 10.1109/CVPRW.2009.5206737
[7]   Staple: Complementary Learners for Real-Time Tracking [J].
Bertinetto, Luca ;
Valmadre, Jack ;
Golodetz, Stuart ;
Miksik, Ondrej ;
Torr, Philip H. S. .
2016 IEEE CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2016, :1401-1409
[8]   Target Response Adaptation for Correlation Filter Tracking [J].
Bibi, Adel ;
Mueller, Matthias ;
Ghanem, Bernard .
COMPUTER VISION - ECCV 2016, PT VI, 2016, 9910 :419-433
[9]  
Bolme DS, 2010, PROC CVPR IEEE, P2544, DOI 10.1109/CVPR.2010.5539960
[10]   BIT: Biologically Inspired Tracker [J].
Cai, Bolun ;
Xu, Xiangmin ;
Xing, Xiaofen ;
Jia, Kui ;
Miao, Jie ;
Tao, Dacheng .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2016, 25 (03) :1327-1339