Online Object Tracking With Sparse Prototypes
Online Object Tracking With Sparse Prototypes
复制标题
使用稀疏原型进行在线对象跟踪
DOI：
10.1109/tip.2012.2202677
复制
发表时间：
2013-01-01
影响因子：
10.6
通讯作者：
Yang, Ming-Hsuan
中科院分区：
文献类型：
--
作者：
Wang, Dong;Lu, Huchuan;Yang, Ming-Hsuan
Online object tracking is a challenging problem as it entails learning an effective model to account for appearance change caused by intrinsic and extrinsic factors. In this paper, we propose a novel online object tracking algorithm with sparse prototypes, which exploits both classic principal component analysis (PCA) algorithms with recent sparse representation schemes for learning effective appearance models. We introduce l(1) regularization into the PCA reconstruction, and develop a novel algorithm to represent an object by sparse prototypes that account explicitly for data and noise. For tracking, objects are represented by the sparse prototypes learned online with update. In order to reduce tracking drift, we present a method that takes occlusion and motion blur into account rather than simply includes image observations for model update. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed tracking algorithm performs favorably against several state-of-the-art methods.
