Robust object tracking using a sparse coadjutant observation model

被引:5
|
作者
Zhao, Jianwei [1 ]
Zhang, Weidong [1 ]
Cao, Feilong [1 ]
机构
[1] China Jiliang Univ, Dept Informat Sci & Math, Hangzhou 310018, Zhejiang, Peoples R China
基金
中国国家自然科学基金;
关键词
Object tracking; Sparse representation; Observation model; Discriminative score model; Generative model; VISUAL TRACKING;
D O I
10.1007/s11042-018-6132-0
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This paper develops a classical visual tracker that is called a discriminative sparse similarity (DSS) tracker. Based on the classical Laplacian multi-task reverse sparse representation to get a DSS map in the DSS tracker, we introduce a sparse generative model (SGM) to handle the appearance variation in the DSS tracker. With the alliance of the DSS map and the SGM, our proposed method can track the object under the occlusion and appearance variations effectively. Numerous experiments on various challenging videos of a tracking benchmark illustrate that the proposed tracker performs favorably against several state-of-the-art trackers.
引用
收藏
页码:30969 / 30991
页数:23
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