13 citations · 13 across the 4 of their papers we have counts for
4 papers
Physics-Informed Tracking (PIT)
Emil Hovad, Allan Peter Engsig-Karup
We propose Physics-Informed Tracking (PIT), a video-based framework for tracking a single particle from video, where a neural network autoencoder localizes a particle as a heatmap…
Extended OpenTT Games Dataset: A table tennis dataset for fine-grained shot type and point outcome
Moamal Fadhil Abdul-Mahdi, Jonas Bruun Hubrechts, Thomas Martini Jørgensen +1
Automatically detecting and classifying strokes in table tennis video can streamline training workflows, enrich broadcast overlays, and enable fine-grained performance analytics. F…
Physics-Informed Holomorphic Neural Networks (PIHNNs): Solving Linear Elasticity Problems
Matteo Calafà, Emil Hovad, Allan P. Engsig-Karup +1
We propose physics-informed holomorphic neural networks (PIHNNs) as a method to solve boundary value problems where the solution can be represented via holomorphic functions. Speci…
Classification of Tennis Actions Using Deep Learning
Emil Hovad, Therese Hougaard-Jensen, Line Katrine Harder Clemmensen
Recent advances of deep learning makes it possible to identify specific events in videos with greater precision. This has great relevance in sports like tennis in order to e.g., au…