A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
Computer Vision and Pattern Recognition
2020-11-13 v2 Machine Learning
Neurons and Cognition
Quantitative Methods
Abstract
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.
Cite
@article{arxiv.2009.00564,
title = {A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives},
author = {Alexander Mathis and Steffen Schneider and Jessy Lauer and Mackenzie W. Mathis},
journal= {arXiv preprint arXiv:2009.00564},
year = {2020}
}
Comments
Review, 21 pages, 8 figures and 5 boxes