Visual tracking based on hierarchical framework and sparse representation
Visual tracking based on hierarchical framework and sparse representation
复制标题
基于层次框架和稀疏表示的视觉跟踪
DOI：
10.1007/s11042-017-5198-4
复制
发表时间：
2018
影响因子：
3.6
通讯作者：
许楚萍
中科院分区：
文献类型：
--
作者：
衣杨;程阳;许楚萍
由于对象跟踪的主要挑战是考虑剧烈的外观变化，因此本文设计了一个利用生成模型和判别模型优势的分层框架。我们的分层框架由三个外观模型组成：基于局部直方图的模型、加权对齐池模型和基于稀疏性的判别模型。基于局部直方图的模型层采用稀疏表示，利用双阈值更新模式考虑局部斑块之间的空间信息来处理遮挡。引入加权对齐池层来对稀疏表示后候选的局部图像块进行加权。与上述两种生成方法不同，全局判别模型层采用候选来稀疏地表示正模板和负模板。之后，开发了一种有效的分层融合策略，通过三个模型的相似性和置信度来融合它们。此外，还提出了三种合理的在线词典和模板更新策略。最后，对当前流行的各种图像序列进行的实验表明，我们提出的跟踪器相对于几种最先进的算法表现良好。
As the main challenge for object tracking is to account for drastic appearance change, a hierarchical framework that exploits the strength of both generative and discriminative models is devised in this paper. Our hierarchical framework consists of three appearance models: local-histogram-based model, weighted alignment pooling model, and sparsity-based discriminative model. Sparse representation is adopted in local-histogram-based model layer that considers the spatial information among local patches with a dual-threshold update schema to deal with occlusion. The weighted alignment pooling layer is introduced to weight the local image patches of the candidates after sparse representation. Different from the above two generative methods, the global discriminant model layer employs candidates to sparsely represent positive and negative templates. After that, an effective hierarchical fusion strategy is developed to fuse the three models via their similarities and the confidence. In addition, three reasonable online dictionary and template update strategies are proposed. Finally, experiments on various current popular image sequences demonstrate that our proposed tracker performs favorably against several state-of-the-art algorithms.