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Toward real-time particle tracking using an event-based dynamic vision sensor

机译:使用基于事件的动态视觉传感器实现实时粒子跟踪

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摘要

Optically based measurements in high Reynolds number fluid flows often require high-speed imaging techniques. These cameras typically record data internally and thus are limited by the amount of onboard memory available. A novel camera technology for use in particle tracking velocimetry is presented in this paper. This technology consists of a dynamic vision sensor in which pixels operate in parallel, transmitting asynchronous events only when relative changes in intensity of approximately 10% are encountered with a temporal resolution of 1 μs. This results in a recording system whose data storage and bandwidth requirements are about 100 times smaller than a typical high-speed image sensor. Post-processing times of data collected from this sensor also increase to about 10 times faster than real time. We present a proof-of-concept study comparing this novel sensor with a high-speed CMOS camera capable of recording up to 2,000 fps at 1,024 × 1,024 pixels. Comparisons are made in the ability of each system to track dense (ρ >1 g/cm3) particles in a solid–liquid two-phase pipe flow. Reynolds numbers based on the bulk velocity and pipe diameter up to 100,000 are investigated.
机译:雷诺数高的流体流动中基于光学的测量通常需要高速成像技术。这些摄像机通常在内部记录数据,因此受到可用板载内存量的限制。本文提出了一种用于粒子跟踪测速的新型相机技术。该技术由动态视觉传感器组成,像素在其中并行运行,仅当在1μs的时间分辨率下遇到大约10%的强度相对变化时,才传输异步事件。这导致记录系统的数据存储和带宽要求比典型的高速图像传感器小约100倍。从该传感器收集的数据的后处理时间也比实时速度快约十倍。我们提供了一项概念验证研究,将这种新型传感器与能够以1,024×1,024像素记录高达2,000 fps的高速CMOS相机进行比较。比较了每个系统在固液两相管道流中跟踪致密(ρ> 1 g / cm 3 )颗粒的能力。研究了基于总速度和最大100,000管直径的雷诺数。

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