Deep-learning optical flow for measuring velocity fields from experimental data

Author:

Tran Phu N.1ORCID,Ray Sattvic2,Lemma Linnea12,Li Yunrui3ORCID,Sweeney Reef2,Baskaran Aparna1ORCID,Dogic Zvonimir124ORCID,Hong Pengyu3ORCID,Hagan Michael F.1ORCID

Affiliation:

1. Department of Physics, Brandeis University, Waltham, MA 02453, USA

2. Department of Physics, University of California at Santa Barbara, Santa Barbara, CA 93106, USA

3. Department of Computer Science, Brandeis University, Waltham, MA 02453, USA

4. Biomolecular and Engineering Science, University of California at Santa Barbara, Santa Barbara, CA 93106, USA

Abstract

Deep learning-based optical flow (DLOF) extracts features in video frames with deep convolutional neural networks to estimate the inter-frame motions of objects. DLOF computes velocity fields more accurately than PIV for densely labeled systems.

Funder

U.S. Department of Energy

Publisher

Royal Society of Chemistry (RSC)

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