This work addresses the issue of motion compensation and pattern tracking in event camera data. An event camera generates asynchronous streams of events triggered independently by each of the pixels upon changes in the observed intensity. Providing great advantages in low-light and rapid-motion scenarios, such unconventional data present significant research challenges as traditional vision algorithms are not directly applicable to this sensing modality. The proposed method decomposes the tracking problem into a local SE(2) motion-compensation step followed by a homography registration of small motion-compensated event batches. The first component relies on Gaussian Process (GP) theory to model the continuous occupancy field of the events in the image plane and embed the camera trajectory in the covariance kernel function. In doing so, estimating the trajectory is done similarly to GP hyperparameter learning by maximising the log marginal likelihood of the data. The continuous occupancy fields are turned into distance fields and used as templates for homography-based registration. By benchmarking the proposed method against other state-of-the-art techniques, we show that our open-source implementation performs high-accuracy motion compensation and produces high-quality tracks in real-world scenarios.

翻译：本文解决了事件相机数据中的运动补偿与模式跟踪问题。事件相机在每个像素观测强度变化时独立触发异步事件流。这种非常规数据在低光照和快速运动场景中具有显著优势，但由于传统视觉算法无法直接应用于该传感模式，带来了重要的研究挑战。所提方法将跟踪问题分解为两步：局部SE(2)运动补偿，以及随后对小批量运动补偿事件进行单应性配准。第一步基于高斯过程理论，在图像平面内建模事件的连续占据场，并将相机轨迹嵌入协方差核函数中。通过最大化数据对数边际似然估计轨迹，类似于GP超参数学习。连续占据场转化为距离场，并作为基于单应性配准的模板。通过将所提方法与其他前沿技术进行基准测试，我们展示了开源实现可在实际场景中实现高精度运动补偿并生成高质量轨迹。