IEEE Trans Image Process. 2018 Oct;27(10):4958-4970. doi: 10.1109/TIP.2018.2848465.
In this paper, we propose a novel local sparse representation-based tracking framework for visual tracking. To deeply mine the appearance characteristics of different local patches, the proposed method divides all local patches of a candidate target into three categories, which are stable patches, valid patches, and invalid patches. All these patches are assigned different weights to consider the different importance of the local patches. For stable patches, we introduce a local sparse score to identify them, and discriminative local sparse coding is developed to decrease the weights of background patches among the stable patches. For valid patches and invalid patches, we adopt local linear regression to distinguish the former from the latter. Furthermore, we propose a weight shrinkage method to determine weights for different valid patches to make our patch weight computation more reasonable. Experimental results on public tracking benchmarks with challenging sequences demonstrate that the proposed method performs favorably against other state-of-the-art tracking methods.
在本文中，我们提出了一种新的基于局部稀疏表示的视觉跟踪框架。为了深入挖掘不同局部补丁的外观特征，所提出的方法将候选目标的所有局部补丁分为三类，即稳定补丁、有效补丁和无效补丁。所有这些补丁都被分配不同的权重，以考虑局部补丁的不同重要性。对于稳定补丁，我们引入了局部稀疏得分来识别它们，并开发了判别性局部稀疏编码来降低稳定补丁中背景补丁的权重。对于有效补丁和无效补丁，我们采用局部线性回归来区分它们。此外，我们提出了一种权重收缩方法来确定不同有效补丁的权重，以使我们的补丁权重计算更加合理。在具有挑战性序列的公共跟踪基准上的实验结果表明，所提出的方法优于其他最先进的跟踪方法。
