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 released for public use. The refined datasets are consolidated and released as the consolidated and refined WorldCup (CARWC) dataset.

翻译：提出了一种新颖的贝叶斯框架，该框架通过仿射变换明确关联相邻视频帧的单应性，同时对关键点不确定性进行显式建模。现有文献虽已在后续帧中采用差分单应性，但未在贝叶斯框架内实现。在应用贝叶斯方法的情形中，摄像机运动未能得到充分建模，且关键点被视作确定性变量。本方法——基于跟踪关键点的贝叶斯单应推断（BHITK）采用双阶段卡尔曼滤波器，显著改进了现有技术。现有关键点检测方法可轻松扩展BHITK模块，使计算复杂度较低、成熟度较低的算法在多数单应评估指标上超越当前最优方法。此外，通过发布的自定义单应标注工具，对WorldCup和TS-WorldCup数据集中的单应标注进行了精细化处理，并将优化后的数据集整合为统一精炼版（CARWC）数据库公开。