论文标题
高速活动摄像头跟踪
High Speed Event Camera TRacking
论文作者
论文摘要
事件摄像机是以微秒为单位的反应时间的生物启发的传感器。该属性使他们呼吁在高度动态的计算机视觉应用中使用。在这项工作中，我们探讨了这种传感技术的限制，并提出了一种超快速跟踪算法，能够以10 kHz的吞吐量估算具有25.8 g的六度运动运动，每秒处理超过一百万个事件。我们的方法能够使用以谎言理论意义提出的错误状态卡尔曼过滤器来跟踪摄像机运动或对象在其前面的运动。该方法包括一种可靠的机制，用于将事件与投影线段相匹配，并具有非常快速的拒绝。对稀疏矩阵的细致处理来实现实时性能。为了比较和性能分析，考虑了不同复杂性的不同运动模型
Event cameras are bioinspired sensors with reaction times in the order of microseconds. This property makes them appealing for use in highly-dynamic computer vision applications. In this work,we explore the limits of this sensing technology and present an ultra-fast tracking algorithm able to estimate six-degree-of-freedom motion with dynamics over 25.8 g, at a throughput of 10 kHz,processing over a million events per second. Our method is capable of tracking either camera motion or the motion of an object in front of it, using an error-state Kalman filter formulated in a Lie-theoretic sense. The method includes a robust mechanism for the matching of events with projected line segments with very fast outlier rejection. Meticulous treatment of sparse matrices is applied to achieve real-time performance. Different motion models of varying complexity are considered for the sake of comparison and performance analysis
