Visual object tracking aims to precisely estimate the bounding box for the
given target, which is a challenging problem due to factors such as deformation
and occlusion. Many recent trackers adopt the multiple-stage tracking strategy
to improve the quality of bounding box estimation. These methods first coarsely
locate the target and then refine the initial prediction in the following
stages. However, existing approaches still suffer from limited precision, and
the coupling of different stages severely restricts the method's
transferability. This work proposes a novel, flexible, and accurate refinement
module called Alpha-Refine (AR), which can significantly improve the base
trackers' box estimation quality. By exploring a series of design options, we
conclude that the key to successful refinement is extracting and maintaining
detailed spatial information as much as possible. Following this principle,
Alpha-Refine adopts a pixel-wise correlation, a corner prediction head, and an
auxiliary mask head as the core components. Comprehensive experiments on
TrackingNet, LaSOT, GOT-10K, and VOT2020 benchmarks with multiple base trackers
show that our approach significantly improves the base trackers' performance
with little extra latency. The proposed Alpha-Refine method leads to a series
of strengthened trackers, among which the ARSiamRPN (AR strengthened SiamRPNpp)
and the ARDiMP50 (ARstrengthened DiMP50) achieve good efficiency-precision
trade-off, while the ARDiMPsuper (AR strengthened DiMP-super) achieves very
competitive performance at a real-time speed. Code and pretrained models are
available at this https URL

本研究提出了一种名为 Alpha-Refine (AR) 的新型、灵活、准确的细化模块，它显著提高了基本跟踪器的框估计质量，通过采用像素级相关、角预测头和辅助掩码头作为核心组件，AR 引导的 DiMP-super 在实时速度下实现了非常有竞争力的性能。

Alpha-Refine: 通过精确的边界框估计提高跟踪性能

Alpha-Refine: Boosting Tracking Performance by Precise Bounding Box  Estimation

For many automated driving functions, a highly accurate perception of the
vehicle environment is a crucial prerequisite. Modern high-resolution radar
sensors generate multiple radar targets per object, which makes these sensors
particularly suitable for the 2D object detection task. This work presents an
approach to detect 2D objects solely depending on sparse radar data using
PointNets. In literature, only methods are presented so far which perform
either object classification or bounding box estimation for objects. In
contrast, this method facilitates a classification together with a bounding box
estimation of objects using a single radar sensor. To this end, PointNets are
adjusted for radar data performing 2D object classification with segmentation,
and 2D bounding box regression in order to estimate an amodal 2D bounding box.
The algorithm is evaluated using an automatically created dataset which consist
of various realistic driving maneuvers. The results show the great potential of
object detection in high-resolution radar data using PointNets.

该论文介绍了使用 PointNets 从稀疏雷达数据中检测 2D 物体的方法，通过段分割和边框回归实现了分类和边框估计，利用高分辨率雷达数据进行物体检测具有巨大潜力。