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A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives

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arxiv 2009.00564 v2 pith:YWGYMQ5T submitted 2020-09-01 cs.CV cs.LGq-bio.NCq-bio.QM

Alexander Mathis , Steffen Schneider , Jessy Lauer , Mackenzie W. Mathis This is my paper · ORCID
classification cs.CVcs.LGq-bio.NCq-bio.QM
keywords deeplearningcapturemotionpitfallsprimerprinciplesadvanced
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Extracting behavioral measurements non-invasively from video is stymied by the fact that it is a hard computational problem. Recent advances in deep learning have tremendously advanced predicting posture from videos directly, which quickly impacted neuroscience and biology more broadly. In this primer we review the budding field of motion capture with deep learning. In particular, we will discuss the principles of those novel algorithms, highlight their potential as well as pitfalls for experimentalists, and provide a glimpse into the future.

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    Risk-averse individuals are more likely to flee conflict areas and risk-tolerant individuals to stay, consistent with a quantile maximization model, in Nigerian panel data.

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