An event-based camera has independent pixels that quickly respond to relative brightness changes and report them asynchronously with high temporal resolution (microsecond level). We present an event-by-event processing framework for sensing the velocity of a moving object and tracking it with an active event-based camera. This paper describes an event-by-event approach for estimating both local velocity and global velocity. Instead of accumulating events over a fixed period of time, we directly estimate the normal velocity of the pixel at which a new event occurs and update the estimate of the global velocity given the new estimate. Our proposed global velocity estimation algorithm is based on the Kalman filter which also provides a confidence measurement for the estimated velocity. Our active tracking system monitors the confidence of the velocity estimate and triggers a tracking command once the confidence reaches a certain threshold. We evaluated our velocity estimation algorithms and the active tracking system in our recently proposed simulator, AESIM. Our results demonstrate that our system can successfully track a moving object with low latency.

中文翻译：

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使用主动事件相机进行速度估计和对象跟踪的逐个事件方法

基于事件的相机具有独立像素，可以快速响应相对亮度变化并以高时间分辨率（微秒级）异步报告它们。我们提出了一个逐事件处理框架，用于感知移动物体的速度并使用基于活动的基于事件的相机对其进行跟踪。本文描述了一种用于估计局部速度和全局速度的逐个事件方法。我们不是在固定时间段内累积事件，而是直接估计发生新事件的像素的正常速度，并在给定新估计的情况下更新全局速度的估计。我们提出的全局速度估计算法基于卡尔曼滤波器，它也为估计的速度提供置信度测量。我们的主动跟踪系统监控速度估计的置信度，并在置信度达到某个阈值时触发跟踪命令。我们在最近提出的模拟器 AESIM 中评估了我们的速度估计算法和主动跟踪系统。我们的结果表明，我们的系统可以以低延迟成功跟踪移动对象。