The Robust Patches-based Tracking Method via Sparse Representation

被引:0
|
作者
Li, Yi [1 ]
He, Zhenyu [1 ]
Yang, Wei-Guo [2 ]
Yi, Shuangyan [1 ]
机构
[1] Harbin Inst Technol, Sch Comp Sci, Shenzhen Grad Sch, Harbin, Peoples R China
[2] Shenzhen Konka Commun Technol Co, Shenzhen, Peoples R China
关键词
VISUAL TRACKING;
D O I
暂无
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Occlusion is one important problem in single object tracking. However, conventional methods are not capable of making full use of the spatial information because of occlusion, which may lead to the drift. In this paper, we propose a robust patches-based tracking method via sparse representation, namely RPSR, which selects the unoccluded patches, and adaptively assigns larger contribution factors to them. The experimental results on popular benchmark video sequences show that our RPSR method is effective and outperforms the state-of-the-art methods for single object tracking.
引用
收藏
页码:109 / 113
页数:5
相关论文
共 50 条
  • [31] Robust object tracking based on sparse representation and incremental weighted PCA
    Xiaofen Xing
    Fuhao Qiu
    Xiangmin Xu
    Chunmei Qing
    Yinrong Wu
    Multimedia Tools and Applications, 2017, 76 : 2039 - 2057
  • [32] Robust Object Tracking Based on Multi-granularity Sparse Representation
    Chu, Honglin
    Wen, Jiajun
    Lai, Zhihui
    INTELLIGENCE SCIENCE AND BIG DATA ENGINEERING: VISUAL DATA ENGINEERING, PT I, 2019, 11935 : 142 - 154
  • [33] Robust object tracking with occlusion handling based on local sparse representation
    Zhao, Hainan
    Wang, Xuan
    Liu, Meng
    International Journal of Signal Processing, Image Processing and Pattern Recognition, 2014, 7 (03) : 407 - 420
  • [34] Robust object tracking based on sparse representation and incremental weighted PCA
    Xing, Xiaofen
    Qiu, Fuhao
    Xu, Xiangmin
    Qing, Chunmei
    Wu, Yinrong
    MULTIMEDIA TOOLS AND APPLICATIONS, 2017, 76 (02) : 2039 - 2057
  • [35] INTEREST POINTS BASED OBJECT TRACKING VIA SPARSE REPRESENTATION
    Babu, R. Venkatesh
    Parate, Priti
    2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2013, : 2963 - 2967
  • [36] Patches-based Markov random field model for multiple object tracking under occlusion
    Wu, Mingjun
    Peng, Xianrong
    Zhang, Qiheng
    Zhao, Rujin
    SIGNAL PROCESSING, 2010, 90 (05) : 1518 - 1529
  • [37] Object tracking method based on sparse representation of joint template
    Zhang, Xu-Dong
    Chen, Zhong-Hai
    Hu, Liang-Mei
    Yang, Hui
    Dong, Wen-Jing
    Kongzhi yu Juece/Control and Decision, 2015, 30 (09): : 1696 - 1700
  • [38] An Efficient Misalignment Method for Visual Tracking Based on Sparse Representation
    Jiang, Shan
    Han, Cheng
    Di, Xiaoqiang
    IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS, 2018, E101D (08): : 2123 - 2131
  • [39] Robust visual tracking via nonlocal regularized multi-view sparse representation
    Kang, Bin
    Zhu, Wei-Ping
    Liang, Dong
    Chen, Mingkai
    PATTERN RECOGNITION, 2019, 88 : 75 - 89
  • [40] ROBUST OBJECT TRACKING VIA SPARSE REPRESENTATION BASED ON COMPRESSIVE COLLABORATIVE HAAR-LIKE FEATURE SPACE
    Zhao Ming
    Qian Han-ming
    Rong Ying-jiao
    Chen Guo
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), 2016, : 274 - 278