Event-based cameras are popular for tracking fast-moving objects due to their high temporal resolution, low latency, and high dynamic range. In this article, we propose a novel algorithm for tracking event blobs using raw events asynchronously in real time. We introduce the concept of an event blob as a spatio-temporal likelihood of event occurrence where the conditional spatial likelihood is blob-like. Many real-world objects, such as car headlights or any quickly moving foreground objects, generate event blob data. The proposed algorithm uses a nearest neighbor classifier with a dynamic threshold criteria for data association coupled with an extended Kalman filter to track the event blob state. Our algorithm achieves highly accurate blob tracking, velocity estimation, and shape estimation even under challenging lighting conditions and high-speed motions ( $>$ 11 000 pixels/s). The microsecond time resolution achieved means that the filter output can be used to derive secondary information, such as time-to-contact or range estimation, that will enable applications to real-world problems, such as collision avoidance, in autonomous driving.

中文翻译：

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用于事件摄像机的异步 Blob 跟踪器


基于事件的相机因其高时间分辨率、低延迟和高动态范围而广受用于跟踪快速移动的物体。在本文中，我们提出了一种新的算法，用于实时异步使用原始事件跟踪事件 blob。我们引入了事件 blob 的概念，作为事件发生的时空可能性，其中条件空间似然是 blob 状的。许多实际对象（如汽车前灯或任何快速移动的前景对象）都会生成事件 blob 数据。所提出的算法使用具有数据关联的动态阈值标准的最近邻分类器，以及扩展的卡尔曼滤波器来跟踪事件 blob 状态。即使在具有挑战性的照明条件和高速运动（ $>$ 11 000 像素/秒）下，我们的算法也能实现高精度的斑点跟踪、速度估计和形状估计。实现的微秒级时间分辨率意味着滤波器输出可用于获取辅助信息，例如接触时间或距离估计，这将使应用程序能够解决自动驾驶中的实际问题，例如避免碰撞。