.png){kind=icon}
返回
Robust Object Tracking via Sparse Collaborative Appearance Model
.png){kind=icon}
OA
AI
DOI：10.1109/TIP.2014.2313227.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this paper, we propose a robust object tracking algorithm based on a sparse collaborative model that exploits both holistic templates and local representations to account for drastic appearance changes. Within the proposed collaborative appearance model, we develop a sparse discriminative classifier (SDC) and sparse generative model (SGM) for object tracking. In the SDC module, we present a classifier that separates the foreground object from the background based on holistic templates. In the SGM module, we propose a histogram-based method that takes the spatial information of each local patch into consideration. The update scheme considers both the most recent observations and original templates, thereby enabling the proposed algorithm to deal with appearance changes effectively and alleviate the tracking drift problem. Numerous experiments on various challenging videos demonstrate that the proposed tracker performs favorably against several state-of-the-art algorithms.
Keyword:
Object tracking
collaborative model
sparse representation
feature selection
occlusion handling
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
机构
引用论文
PTX encapsulated by an XG–DOX conjugate for combination therapy against multi-drug resistance.png)
RSC Advances
RSC AdvancesIF0
PREAI
分享收藏
分享收藏分享收藏
查看更多内容