基于视频的序列贝叶斯单应性估计用于足球场地注册
Video-based Sequential Bayesian Homography Estimation for Soccer Field Registration
AI总结：
本文提出BHITK方法，通过两阶段卡尔曼滤波在贝叶斯框架下对视频帧间单应性和关键点不确定性进行序列建模，使低成本方法超越现有最优方法，并发布精化整合的CARWC足球场数据集。
摘要：
本文提出了一种新颖的贝叶斯框架，该框架通过仿射变换显式地将一帧视频的单应性与下一帧相关联，同时显式地对关键点不确定性进行建模。以往文献在相邻帧之间使用了差分单应性，但并未在贝叶斯设定下使用。在已应用贝叶斯方法的情形中，相机运动未被充分建模，且关键点被视为确定性的。所提出的方法，即基于跟踪关键点的贝叶斯单应性推断（BHITK），采用两阶段卡尔曼滤波器，并显著改进了现有方法。现有的关键点检测方法可以容易地通过BHITK进行增强。它使较不复杂且计算成本较低的方法能够在大多数单应性评估指标上超越最先进的方法。此外，WorldCup和TS-WorldCup数据集的单应性标注已使用一个自定义单应性标注工具进行了精化，该工具已公开发布供公众使用。精化后的数据集被整合并发布为整合精化WorldCup（CARWC）数据集。
英文摘要：
A novel Bayesian framework is proposed, which explicitly relates the homography of one video frame to the next through an affine transformation while explicitly modelling keypoint uncertainty. The literature has previously used differential homography between subsequent frames, but not in a Bayesian setting. In cases where Bayesian methods have been applied, camera motion is not adequately modelled, and keypoints are treated as deterministic. The proposed method, Bayesian Homography Inference from Tracked Keypoints (BHITK), employs a two-stage Kalman filter and significantly improves existing methods. Existing keypoint detection methods may be easily augmented with BHITK. It enables less sophisticated and less computationally expensive methods to outperform the state-of-the-art approaches in most homography evaluation metrics. Furthermore, the homography annotations of the WorldCup and TS-WorldCup datasets have been refined using a custom homography annotation tool that has been released for public use. The refined datasets are consolidated and released as the consolidated and refined WorldCup (CARWC) dataset.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
