Search Results for author: Joachim Gudmundsson

Found 7 papers, 0 papers with code

Exploring Sub-skeleton Trajectories for Interpretable Recognition of Sign Language

no code implementations3 Feb 2022 Joachim Gudmundsson, Martin P. Seybold, John Pfeifer

Recent advances in tracking sensors and pose estimation software enable smart systems to use trajectories of skeleton joint locations for supervised learning.

Pose Estimation

Translation Invariant Fréchet Distance Queries

no code implementations11 Feb 2021 Joachim Gudmundsson, André van Renssen, Zeinab Saeidi, Sampson Wong

For some applications, it is desirable to match the curves under translation before computing the Fr\'echet distance between them.

Computational Geometry

A Practical Index Structure Supporting Fréchet Proximity Queries Among Trajectories

no code implementations28 May 2020 Joachim Gudmundsson, Michael Horton, John Pfeifer, Martin P. Seybold

We present a scalable approach for range and $k$ nearest neighbor queries under computationally expensive metrics, like the continuous Fr\'echet distance on trajectory data.

Clustering

A Visual Measure of Changes to Weighted Self-Organizing Map Patterns

no code implementations27 Mar 2017 Younjin Chung, Joachim Gudmundsson, Masahiro Takatsuka

In order to analyze the change, it is important to measure the difference of the patterns.

Spatio-Temporal Analysis of Team Sports -- A Survey

no code implementations22 Feb 2016 Joachim Gudmundsson, Michael Horton

Team-based invasion sports such as football, basketball and hockey are similar in the sense that the players are able to move freely around the playing area; and that player and team performance cannot be fully analysed without considering the movements and interactions of all players as a group.

Other Computer Science A.1; H.2.8

Classification of Passes in Football Matches using Spatiotemporal Data

no code implementations18 Jul 2014 Michael Horton, Joachim Gudmundsson, Sanjay Chawla, Joël Estephan

Experimental results show that we are able to produce a classifier with 85. 8% accuracy on classifying passes as Good, OK or Bad, and that the predictor variables computed using complex methods from computational geometry are of moderate importance to the learned classifiers.

Classification General Classification +1

Computational Aspects of Multi-Winner Approval Voting

no code implementations11 Jul 2014 Haris Aziz, Serge Gaspers, Joachim Gudmundsson, Simon Mackenzie, Nicholas Mattei, Toby Walsh

We study computational aspects of three prominent voting rules that use approval ballots to elect multiple winners.

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