.png){kind=icon}
返回
Object tracking via appearance modeling and sparse representation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2011.08.006.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This paper proposes a robust tracking method by the combination of appearance modeling and sparse representation. In this method, the appearance of an object is modeled by multiple linear subspaces. Then within the sparse representation framework, we construct a similarity measure to evaluate the distance between a target candidate and the learned appearance model. Finally, tracking is achieved by Bayesian inference, in which a particle filter is used to estimate the target state sequentially over time. With the tracking result, the learned appearance model will be updated adaptively. The combination of appearance modeling and sparse representation makes our tracking algorithm robust to most of possible target variations due to illumination changes, pose changes, deformations and occlusions. Theoretic analysis and experiments compared with state-of-the-art methods demonstrate the effectivity of the proposed algorithm. (C) 2011 Elsevier B.V. All rights reserved.
Keyword:
Target variation
Online appearance modeling
Sparse representation
Bayesian inference
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
4.2
论文数:
4.1K
被引数:
6.7K
机构
引用论文
.png){kind=icon}
查看更多内容