Javed Sajid, Mahmood Arif, Dias Jorge, Seneviratne Lakmal, Werghi Naoufel
IEEE Trans Cybern. 2022 Nov;52(11):12259-12274. doi: 10.1109/TCYB.2021.3086194. Epub 2022 Oct 17.
Visual object tracking is a fundamental and challenging task in many high-level vision and robotics applications. It is typically formulated by estimating the target appearance model between consecutive frames. Discriminative correlation filters (DCFs) and their variants have achieved promising speed and accuracy for visual tracking in many challenging scenarios. However, because of the unwanted boundary effects and lack of geometric constraints, these methods suffer from performance degradation. In the current work, we propose hierarchical spatiotemporal graph-regularized correlation filters for robust object tracking. The target sample is decomposed into a large number of deep channels, which are then used to construct a spatial graph such that each graph node corresponds to a particular target location across all channels. Such a graph effectively captures the spatial structure of the target object. In order to capture the temporal structure of the target object, the information in the deep channels obtained from a temporal window is compressed using the principal component analysis, and then, a temporal graph is constructed such that each graph node corresponds to a particular target location in the temporal dimension. Both spatial and temporal graphs span different subspaces such that the target and the background become linearly separable. The learned correlation filter is constrained to act as an eigenvector of the Laplacian of these spatiotemporal graphs. We propose a novel objective function that incorporates these spatiotemporal constraints into the DCFs framework. We solve the objective function using alternating direction methods of multipliers such that each subproblem has a closed-form solution. We evaluate our proposed algorithm on six challenging benchmark datasets and compare it with 33 existing state-of-the art trackers. Our results demonstrate an excellent performance of the proposed algorithm compared to the existing trackers.
视觉目标跟踪是许多高级视觉和机器人应用中的一项基本且具有挑战性的任务。它通常通过估计连续帧之间的目标外观模型来制定。判别相关滤波器（DCF）及其变体在许多具有挑战性的场景中实现了视觉跟踪的速度和精度。然而，由于存在不必要的边界效应和缺乏几何约束，这些方法存在性能下降的问题。在当前的工作中，我们提出了用于鲁棒目标跟踪的分层时空图正则化相关滤波器。目标样本被分解为大量的深度通道，然后用于构建空间图，使得每个图节点对应于所有通道上的特定目标位置。这样的图有效地捕获了目标对象的空间结构。为了捕获目标对象的时间结构，使用主成分分析对从时间窗口获得的深度通道中的信息进行压缩，然后构建时间图，使得每个图节点对应于时间维度上的特定目标位置。空间图和时间图都跨越不同的子空间，使得目标和背景变得线性可分。学习到的相关滤波器被约束为这些时空图的拉普拉斯算子的特征向量。我们提出了一种新颖的目标函数，将这些时空约束纳入DCF框架。我们使用乘子交替方向法求解目标函数，使得每个子问题都有一个封闭形式的解。我们在六个具有挑战性的基准数据集上评估我们提出的算法，并将其与33种现有的先进跟踪器进行比较。我们的结果表明，与现有跟踪器相比，所提出的算法具有出色的性能。
