Many astonishing correlation filter trackers pay limited concentration on the tracking reliability and locating accuracy. To solve the issues, we propose a reliable and accurate cross correlation particle filter tracker via graph regularized multi-kernel multi-subtask learning. Specifically, multiple non-linear kernels are assigned to multi-channel features with reliable feature selection. Each kernel space corresponds to one type of reliable and discriminative features. Then, we define the trace of each target subregion with one feature as a single view, and their multi-view cooperations and interdependencies are exploited to jointly learn multi-kernel subtask cross correlation particle filters, and make them complement and boost each other. The learned filters consist of two complementary parts: weighted combination of base kernels and reliable integration of base filters. The former is associated to feature reliability with importance map, and the weighted information reflects different tracking contribution to accurate location. The second part is to find the reliable target subtasks via the response map, to exclude the distractive subtasks or backgrounds. Besides, the proposed tracker constructs the Laplacian graph regularization via cross similarity of different subtasks, which not only exploits the intrinsic structure among subtasks, and preserves their spatial layout structure, but also maintains the temporal-spatial consistency of subtasks. Comprehensive experiments on five datasets demonstrate its remarkable and competitive performance against state-of-the-art methods.

中文翻译：

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可靠的多内核子任务图相关跟踪器


许多令人惊叹的相关滤波器跟踪器对跟踪可靠性和定位精度的关注有限。为了解决这些问题，我们通过图正则化多核多子任务学习提出了一种可靠且准确的互相关粒子滤波器跟踪器。具体来说，多个非线性内核被分配给具有可靠特征选择的多通道特征。每个内核空间对应于一种类型的可靠且有区别的特征。然后，我们将具有一个特征的每个目标子区域的轨迹定义为单个视图，并利用它们的多视图协作和相互依赖来共同学习多核子任务互相关粒子滤波器，并使它们相互补充和促进。学习到的过滤器由两个互补的部分组成：基本内核的加权组合和基本过滤器的可靠集成。前者与重要度图的特征可靠性相关联，加权信息反映了不同的跟踪对准确定位的贡献。第二部分是通过响应图找到可靠的目标子任务，排除分散注意力的子任务或背景。此外，所提出的跟踪器通过不同子任务的交叉相似性构建拉普拉斯图正则化，不仅利用了子任务之间的内在结构，保留了它们的空间布局结构，而且还保持了子任务的时空一致性。对五个数据集的综合实验证明了其与最先进的方法相比具有卓越的和有竞争力的性能。