.png){kind=icon}
返回
Inverse Sparse Group Lasso Model for Robust Object Tracking
.png){kind=icon}
PRE
AI
DOI：10.1109/TMM.2017.2689918.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sparse representation has been applied to visual tracking. The visual tracking models based on sparse representation use a template set as dictionary atoms to reconstruct candidate samples without considering similarity among atoms. In this paper, we present a robust tracking method based on the inverse sparse group lasso model. Our method exploits both the group structure of similar candidate samples and the local structure between templates and samples. Unlike the conventional sparse representation, the templates are encoded by the candidate samples, and similar samples are selected to reconstruct the template at the group level, which facilitates inter-group sparsity. Every sample group achieves the intra-group sparsity so that the information between the related dictionary atoms is taken into account. Moreover, the local structure between templates and samples is considered to build the reconstruction model, which ensures that the computed coefficients similarity is consistent with the similarity between templates and samples. A gradient descent-based optimization method is employed and a sparse mapping table is obtained using the coefficient matrix and hash-distance weight matrix. Experiments were conducted with publicly available datasets and a comparison study was performed against 20 state-of-the-art methods. Both qualitative and quantitative results are reported. The proposed method demonstrated improved robustness and accuracy and exhibited comparable computational complexity.
Keyword:
Computer vision
hash distance
sparse coding
sparse group lasso
visual tracking
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:
9.7
论文数:
4.5K
被引数:
2.4W
机构
引用论文
.png){kind=icon}
Graphene/Ionic Liquid Binary Electrode Material for High Performance Supercapacitor用于高性能超级电容器的石墨烯/离子液体二元电极材料
PREAI
分享收藏分享收藏
查看更多内容