论文标题
事件摄像头数据驱动的功能跟踪
Data-driven Feature Tracking for Event Cameras
论文作者
论文摘要
由于它们具有高度的时间分辨率，对运动模糊的弹性提高，并且输出非常稀疏，因此事件摄像头已被证明是低延迟和低型带宽功能跟踪的理想选择，即使在具有挑战性的情况下也是如此。现有的事件摄像机的功能跟踪方法是手工制作的或源自第一原理，但需要广泛的参数调整，对噪声敏感，并且由于未建模的效果而不会概括到不同的情况。为了解决这些缺陷，我们介绍了第一个用于事件摄像机的数据驱动的功能跟踪器，该功能摄像机利用低延迟事件来跟踪在灰度框架中检测到的功能。我们通过一个新型的框架注意模块实现了强大的性能，该模块在特征轨道上共享信息。通过将零射击从合成数据直接传输到真实数据，我们的数据驱动的跟踪器在相对特征年龄的现有方法中的表现高达120％，同时也达到了最低的延迟。通过通过新颖的自学策略调整我们的跟踪器，将跟踪器改编成真实数据，从而进一步增加了130％。
Because of their high temporal resolution, increased resilience to motion blur, and very sparse output, event cameras have been shown to be ideal for low-latency and low-bandwidth feature tracking, even in challenging scenarios. Existing feature tracking methods for event cameras are either handcrafted or derived from first principles but require extensive parameter tuning, are sensitive to noise, and do not generalize to different scenarios due to unmodeled effects. To tackle these deficiencies, we introduce the first data-driven feature tracker for event cameras, which leverages low-latency events to track features detected in a grayscale frame. We achieve robust performance via a novel frame attention module, which shares information across feature tracks. By directly transferring zero-shot from synthetic to real data, our data-driven tracker outperforms existing approaches in relative feature age by up to 120% while also achieving the lowest latency. This performance gap is further increased to 130% by adapting our tracker to real data with a novel self-supervision strategy.
