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Estimating particle velocity from dual-camera mixed reality video images using 3D particle tracking velocimetry
Chivers, Thomas
Chivers, Thomas
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Abstract
Mixed reality (MR) systems integrate diverse sensors, allowing users to better visualize and quantify surrounding environmental processes. Some existing mixed reality headsets include synchronized front-facing cameras that, among other things, can be used to track naturally occurring tracer particles (such as dust or snowflakes) to estimate particle velocity field in real time. The current work presents a 3D particle tracking velocimetry (PTV) method for use with MR systems, which combines various monocular cues to match particles between corresponding stereo images. Binocular disparity is used to estimate particle distance from an observer. Individual particles are tracked through time and used to construct the vector field of a scene. A digital display of velocity vectors can be broadcasted into a user’s surrounding environment with the MR headset to be used as a flow visualization tool. The mixed reality particle tracking velocimetry (MR-PTV) approach was optimized to perform in natural conditions where particle size, particle color, and lighting are non-uniform. The approach was first tested using synthetic particle image data obtained by discrete element method simulations then experimentally validated for particles transported by a flume flow using the Microsoft HoloLens 2 MR headset. Uniform flow and flow around a body were considered experimentally. Experimental velocity measurements are compared to computational fluid dynamics results. The resulting MR-PTV system can be used for a variety of industry, scientific and recreational purposes for field-based measurement of particle velocities in real time.
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Date
2023-01-01