Charles L. Brown Department of Electrical and Computer Engineering, University of Virginia, Charlottesville, VA 22904-4743, USA.
Sensors (Basel). 2022 Jan 4;22(1):354. doi: 10.3390/s22010354.
Accurate and robust scale estimation in visual object tracking is a challenging task. To obtain a scale estimation of the target object, most methods rely either on a multi-scale searching scheme or on refining a set of predefined anchor boxes. These methods require heuristically selected parameters, such as scale factors of the multi-scale searching scheme, or sizes and aspect ratios of the predefined candidate anchor boxes. On the contrary, a centerness-aware anchor-free tracker (CAT) is designed in this work. First, the location and scale of the target object are predicted in an anchor-free fashion by decomposing tracking into parallel classification and regression problems. The proposed anchor-free design obviates the need for hyperparameters related to the anchor boxes, making CAT more generic and flexible. Second, the proposed centerness-aware classification branch can identify the foreground from the background while predicting the normalized distance from the location within the foreground to the target center, i.e., the centerness. The proposed centerness-aware classification branch improves the tracking accuracy and robustness significantly by suppressing low-quality state estimates. The experiments show that our centerness-aware anchor-free tracker, with its appealing features, achieves salient performance in a wide variety of tracking scenarios.
在视觉目标跟踪中，准确和鲁棒的尺度估计是一项具有挑战性的任务。为了获得目标物体的尺度估计，大多数方法要么依赖于多尺度搜索方案，要么依赖于对一组预定义的候选锚框进行细化。这些方法需要启发式选择的参数，例如多尺度搜索方案的尺度因子，或预定义候选锚框的大小和纵横比。相反，本文设计了一种中心感知无锚点跟踪器（CAT）。首先，通过将跟踪分解为并行的分类和回归问题，以无锚点的方式预测目标物体的位置和尺度。所提出的无锚点设计避免了与锚框相关的超参数的需要，使 CAT 更加通用和灵活。其次，所提出的中心感知分类分支可以在预测从前景内位置到目标中心的归一化距离（即中心度）的同时，从背景中识别出前景。所提出的中心感知分类分支通过抑制低质量的状态估计，显著提高了跟踪的准确性和鲁棒性。实验表明，我们的中心感知无锚点跟踪器具有吸引人的特点，在各种跟踪场景中都取得了显著的性能。
