The "AR_Tag-VR_Projection" project is designed to process AR tags within video streams to perform advanced visual augmentations. Using Python and OpenCV, this system processes video inputs to isolate dynamic features via FFT, overlays 2D images, and then projects 3D graphics onto detected AR tags.
Extracts low speed segments from spatiotemporal trajectories using moving median of speed. Fast and robust. Adaptively determines the parameters from the data, instead of setting objective, arbitrary parameters. Each trajectory in a set of trajectories will have unique subjective parameters.
Zeta Collapse Model (ZCM). System architecture and database are patent-pending and not included.
📉 Implement the Zeta Collapse Model to effectively isolate stable data subsets in noisy environments without relying on machine learning or statistics.
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