Group evolution patterns in running races
| Autor: | Matias Korman, Marta Fort, Joan Antoni Sellarès, Yago Diez |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
Computational Geometry (cs.CG)
FOS: Computer and information sciences Information Systems and Management Theoretical computer science Computer science 02 engineering and technology Geometria computacional Computational geometry Theoretical Computer Science Set (abstract data type) Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Algorismes computacionals Group (mathematics) 05 social sciences Process (computing) 050301 education Computer algorithms Computer Science Applications Transformation (function) Control and Systems Engineering Computer Science - Computational Geometry 020201 artificial intelligence & image processing Focus (optics) 0503 education Software |
| Zdroj: | © Information Sciences, 2019, vol. 479, p. 20-39 Articles publicats (D-IMAE) Díez Donoso, Santiago Fort, Marta Korman, Matias Sellarès i Chiva, Joan Antoni 2019 Group evolution patterns in running races Information Sciences 479 20 39 DUGiDocs – Universitat de Girona instname |
| Popis: | We address the problem of tracking and detecting interactions between the different groups of runners that form during a race. In athletic races control points are set to monitor the progress of athletes over the course. Intuitively, a {\it group} is a sufficiently large set of athletes that cross a control point together. After adapting an existing definition of group to our setting we go on to study two types of group evolution patterns. The primary focus of this work are {\it evolution patterns}, i.e. the transformation and interaction of groups of athletes between two consecutive control points. We provide an accurate geometric model of the following evolution patterns: survives, appears, disappears, expands, shrinks, merges, splits, coheres and disbands, and present algorithms to efficiently compute these patterns. Next, based on the algorithms introduced for identifying evolution patterns, algorithms to detect {\it long-term patterns} are introduced. These patterns track global properties over several control points: surviving, traceable forward, traceable backward and related forward and backward. Experimental evaluation of the algorithms provided is presented using real and synthetic data. Using the data currently available, our experiments show how our algorithms can provide valuable insight into how running races develop. Moreover, we also show how, even if dense (synthetic) data is considered, our algorithms are also able to process it in real time. |
| Databáze: | OpenAIRE |
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