SCRM: self-correlated representation model for visual tracking

被引：1
作者：
Jiang, Shengqin [1 ,3 ]
Lu, Xiaobo [1 ,3 ]
Cheng, Fengna [2 ]
机构：
[1] Southeast Univ, Sch Automat, Nanjing 210096, Peoples R China
[2] Nanjing Forestry Univ, Coll Mech & Elect Engn, Nanjing 210037, Peoples R China
[3] Southeast Univ, Key Lab Measurement & Control Complex Syst Engn, Minist Educ, Nanjing 210096, Peoples R China
来源：
SOFT COMPUTING | 2020年 / 24卷 / 03期
基金：
中国国家自然科学基金;
关键词：
Visual tracking; Self-correlated representation; Low-dimensional subspace; Particle filter; FEATURE-SELECTION; OBJECT TRACKING; SPARSITY;
D O I：
10.1007/s00500-019-04052-w
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
y Sparse representation (SR) as a seminal model for visual tracking explores the relationship between all candidates and the observed templates. Different from SR-based trackers, we propose a self-correlated representation model for robust visual tracking. Firstly, we learn a low-dimensional subspace representation from highly correlated templates to model the object, which aims at eliminating the redundant information and reducing the influence of noisy templates. Then, we represent the subspace by itself to learn the inner underlying features from subspace vectors. To further enhance model's discriminating power, a new observation model is developed by considering both error distribution and large outliers. Experiments are conducted on some challenging video clips and demonstrate the favorable performance of our tracking system compared to some state-of-the-art representation-based trackers.
引用
收藏
页码：2187 / 2199
页数：13
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