Finding Similar Movements in Positional Data Streams

Publikation: Beiträge in SammelwerkenAufsätze in KonferenzbändenForschungbegutachtet

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In this paper, we study the problem of efficiently finding similar movements in positional data streams, given a query trajectory. Our approach is based on a translation-, rotation-, and scale-invariant representation of movements. Near-neighbours given a query trajectory are then efficiently computed using dynamic time warping and locality sensitive hashing. Empirically, we show the efficiency and accuracy of our approach on positional data streams recorded from a real soccer game.

OriginalspracheEnglisch
TitelMachine Learning and Data Mining for Sports Analytics - MLSA 2013 : Proceedings
HerausgeberDavis Jesse, Jan Van Haaren, Albrecht Zimmermann
Anzahl der Seiten9
ErscheinungsortPrag
VerlagSun Site Central Europe (RWTH Aachen University)
Erscheinungsdatum2013
Seiten49-57
PublikationsstatusErschienen - 2013
Extern publiziertJa
VeranstaltungEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases - ECMLPKDD 2013 - Prag, Tschechische Republik
Dauer: 23.09.201327.09.2013
http://www.ecmlpkdd2013.org/

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