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. Nearneighbours 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.
© Copyright 2013 Machine Learning and Data Mining for Sports Analytics ECML/PKDD 2013 workshop. Veröffentlicht von Department of Computer Science, KU Leuven. Alle Rechte vorbehalten.