Three-dimensional particle tracking velocimetry using dynamic vision sensors

Borer, David; Delbrück, Tobias; Rösgen, Thomas

doi:10.1007/s00348-017-2452-5

Cited by 22 publications

(20 citation statements)

References 6 publications

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“…Laskin nozzle generated aerosol droplets, typical for PIV experiments, had insufficient scattering due to the volume illumination and high frame rates of 3D-PTV. It is noteworthy that helium filled soap bubbles are environmentally friendly alternative for tracer particles, recently demonstrated sufficiently large production rates, sufficient scattering characteristics and neutral buoyancy 33,59 .…”

Section: Methodsmentioning

confidence: 99%

“…small Δ r ), yet they do not solve the data transfer rate bottleneck and are limited for short experimental recordings. Neuromorphic cameras 33 were recently utilized to track helium filled soap bubbles in a wind tunnel, providing high temporal rates, yet their sensitivity is insufficient for small tracers to be identified over the complex background illumination in the canopy flow. Real-time image compression techniques based on image binarization 34 or edge detection 35 were suggested in the past to mitigate the data transfer rates, however, these methods are effective only in ideal cases where tracers can be trivially segmented on a uniformly dark background.…”

Section: Introductionmentioning

confidence: 99%

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Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

Shnapp

Shapira²,

Peri

et al. 2019

Sci Rep

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Direct estimation of Lagrangian turbulence statistics is essential for the proper modeling of dispersion and transport in highly obstructed canopy flows. However, Lagrangian flow measurements demand very high rates of data acquisition, resulting in bottlenecks that prevented the estimation of Lagrangian statistics in canopy flows hitherto. We report on a new extension to the 3D Particle Tracking Velocimetry (3D-PTV) method, featuring real-time particle segmentation that outputs centroids and sizes of tracer particles and performed on dedicated hardware during high-speed digital video acquisition from multiple cameras. The proposed extension results in four orders of magnitude reduction in data transfer rate that enables to perform substantially longer experimental runs, facilitating measurements of convergent statistics. The extended method is demonstrated through an experimental wind tunnel investigation of the Lagrangian statistics in a heterogeneous canopy flow. We observe that acceleration statistics are affected by the mean shear at the top of the canopy layer and that Lagrangian particle dispersion at small scales is dominated by turbulence in the wake of the roughness elements. This approach enables to overcome major shortcomings from Eulerian-based measurements which rely on assumptions such as the Taylor’s frozen turbulence hypothesis, which is known to fail in highly turbulent flows.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

Shnapp

Shapira²,

Peri

et al. 2019

Sci Rep

View full text Add to dashboard Cite

show abstract

“…After 2006, there is steady increase in the research on the subject, indicating that the potential of the technology has been discovered. (Etienne-Cummings and Van der Spiegel, 1996;Delbruck and Liu, 2012;Posch et al, 2014;Gallego et al, 2020), Period of 2002(Delbruck, 2016, neuromorphic chips , VLSI neuromorphic circuits (Indiveri, 2008), spiking neural networks (Brette et al, 2007) Benchmarks Guidelines for benchmark creation (Tan et al, 2015), dataset (Gibson et al, 2014a;Li et al, 2017), object recognition (Serrano-Gotarredona and Linares-Barranco, 2015), action recognition and tracking (Hu et al, 2016), 3D perception (Zhu et al, 2018), driving applications (Binas et al, 2017) Applications Tracking Object tracking (Gómez-Rodríguez et al, 2011;Saner et al, 2014;Delbruck et al, 2015;Zong et al, 2018), multiple object (Gómez-Rodríguez et al, 2010;Linares-Barranco et al, 2015), camera movement (Kim et al, 2008;Reinbacher et al, 2017), feature tracking (Lagorce et al, 2015b;Ni et al, 2015;Alzugaray and Chli, 2018a), stereo tracking (Schraml et al, 2010b;Müller and Conradt, 2012), camera pose (Gallego et al, 2015(Gallego et al, , 2016(Gallego et al, , 2018aMueggler et al, 2015c), micro-particle tracking (Drazen et al, 2011;Borer et al, 2017), subatomic particle tracking (Neri et al, 2015(Neri et al, , 2017, car tracking (Litzenberger et...…”

Section: Summary Of Reviewed Researchmentioning

confidence: 99%

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Tayarani-Najaran

Schmuker

2021

Front. Neural Circuits

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The nervous systems converts the physical quantities sensed by its primary receptors into trains of events that are then processed in the brain. The unmatched efficiency in information processing has long inspired engineers to seek brain-like approaches to sensing and signal processing. The key principle pursued in neuromorphic sensing is to shed the traditional approach of periodic sampling in favor of an event-driven scheme that mimicks sampling as it occurs in the nervous system, where events are preferably emitted upon the change of the sensed stimulus. In this paper we highlight the advantages and challenges of event-based sensing and signal processing in the visual, auditory and olfactory domains. We also provide a survey of the literature covering neuromorphic sensing and signal processing in all three modalities. Our aim is to facilitate research in event-based sensing and signal processing by providing a comprehensive overview of the research performed previously as well as highlighting conceptual advantages, current progress and future challenges in the field.

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“…The proposed algorithm is suitable only for the 2D tracking of a sparse set of fully resolved particles (10 pixels diameter) in the volume. Recently, [11] proposed another approach based on Kalman filters to track neutrally buoyant soap bubbles from three cameras. This technique can handle the tracking and 3D reconstruction of under-resolved particles, which allows the larger studied volumes.…”

Section: Related Workmentioning

confidence: 99%

Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction

Wang

Idoughi

Heidrich

2020

Lecture Notes in Computer Science

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Existing Particle Imaging Velocimetry techniques require the use of high-speed cameras to reconstruct time-resolved fluid flows. These cameras provide high-resolution images at high frame rates, which generates bandwidth and memory issues. By capturing only changes in the brightness with a very low latency and at low data rate, event-based cameras have the ability to tackle such issues. In this paper, we present a new framework that retrieves dense 3D measurements of the fluid velocity field using a pair of event-based cameras. First, we track particles inside the two event sequences in order to estimate their 2D velocity in the two sequences of images. A stereo-matching step is then performed to retrieve their 3D positions. These intermediate outputs are incorporated into an optimization framework that also includes physically plausible regularizers, in order to retrieve the 3D velocity field. Extensive experiments on both simulated and real data demonstrate the efficacy of our approach.

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Three-dimensional particle tracking velocimetry using dynamic vision sensors

Cited by 22 publications

References 6 publications

Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

Extended 3D-PTV for direct measurements of Lagrangian statistics of canopy turbulence in a wind tunnel

Event-Based Sensing and Signal Processing in the Visual, Auditory, and Olfactory Domain: A Review

Stereo Event-Based Particle Tracking Velocimetry for 3D Fluid Flow Reconstruction

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