...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>IEEE Transactions on Circuits and Systems for Video Technology >Robust Visual Tracking via Sparse Representation Under Subclass Discriminant Constraint
【24h】

Robust Visual Tracking via Sparse Representation Under Subclass Discriminant Constraint

机译：在子类判别约束下通过稀疏表示进行鲁棒的视觉跟踪

获取原文
获取原文并翻译 | 示例
   

获取外文期刊封面封底 >>

       
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

In this paper, we propose a method for visual tracking based on local sparse representation. Image patches from the object and the background are split into image blocks to construct local representations. Within the subclass discriminant framework, a discriminative subspace is learned to distinguish the object image blocks from the background image blocks while preserving their multimodal structure. A dictionary is constructed using the centers of the object subclasses. With this dictionary, sparse coding is implemented on the projected vectors corresponding to the image blocks, and the sparse coefficients are concatenated to obtain a local sparse code as the feature that represents the image patch. Considering the subclass discriminant constraint and the sparsity constraint imposed on the sparse coding, the subspace learning and sparse representation problems are converted into a joint optimization problem with respect to a transformation matrix and sparse coefficients. To enhance the tracking accuracy, two dictionaries are devised, one to incorporate the original observations of the target and the other to incorporate the latest observations, thereby providing two templates to characterize the appearance of the target. Histogram intersection over the local sparse codes provides an evaluation of the confidence. Finally, the candidate with the maximal confidence is selected as the object image patch. Compared with several state-of-the-art algorithms, our method demonstrates a superior performance when applied to challenging sequences.
机译：本文提出了一种基于局部稀疏表示的视觉跟踪方法。来自对象和背景的图像块被分成图像块以构造局部表示。在子类判别框架内，学习判别子空间以区分对象图像块和背景图像块，同时保留其多峰结构。使用对象子类的中心构造字典。利用该字典，在与图像块相对应的投影矢量上执行稀疏编码，并且将稀疏系数进行级联以获得局部稀疏码作为表示图像块的特征。考虑到对稀疏编码施加的子类判别约束和稀疏约束，将子空间学习和稀疏表示问题转换为关于变换矩阵和稀疏系数的联合优化问题。为了提高跟踪精度，设计了两个词典，一个词典合并了目标的原始观测值，另一个词典合并了最新的观测值，从而提供了两个模板来表征目标的外观。局部稀疏代码上的直方图交集提供了置信度的评估。最后，选择具有最大置信度的候选对象作为目标图像块。与几种最先进的算法相比，我们的方法在应用于具有挑战性的序列时表现出了卓越的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号