Tracking 3-D Motion using Deep Learning and a Structure-Aware Temporal Bilateral Filter

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Researchers at Tohoku University have proposed a novel approach to recapture 3-D motion data from a flexible magnetic flux sensor array, using deep learning and a structure-aware temporal bilateral filter. This approach has made tracking activities more efficient. It can track various movements, including fingers manipulating objects, beetles moving inside a vivarium with leaves and soil, and opaque fluid flow.

The challenges associated with 3-D tracking

Computing the 3D configuration of markers from flux sensor data is challenging. This is because the existing numerical methods experience system noise, dead angles, the need for initialization, and constraints in the sensor array’s layout.



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