An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors

Nakanishi, Ryota; Maeno, Junya; Murakami, Kazuhito; Naruse, Tadashi

doi:10.1007/978-3-642-11876-0_20

Cited by 12 publications

(11 citation statements)

References 5 publications

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“…To achieve the real-time computation required for application in the RoboCup robot football tournament [47], the authors proposed an alternative approach. Instead of computing the time required for every player to get to every point, Nakanishi et al [59] used a so-called reachable polygonal region (RPR). The RPR of a player p given time t is the region that p can reach within time t. An advantage with using the RPR for computing dominant regions is that more complex motion models can be used by simply drawing the RPR for different values of t. They presented the following high-level algorithm.…”

Section: Dominant Regionsmentioning

confidence: 99%

Spatio-Temporal Analysis of Team Sports

2017

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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. State of the art object tracking systems now produce spatio-temporal traces of player trajectories with high definition and high frequency, and this, in turn, has facilitated a variety of research efforts, across many disciplines, to extract insight from the trajectories. We survey recent research efforts that use spatio-temporal data from team sports as input, and involve non-trivial computation. This article categorises the research efforts in a coherent framework and identifies a number of open research questions.

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Section: Dominant Regionsmentioning

confidence: 99%

Spatio-Temporal Analysis of Team Sports

2017

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“…The dominant region considers the direction and velocity of the players [3]. This concept is further developed by Fujimura and Sugihara [5] and Nakanishi et al [15] who define efficient approximation models for calculating the dominant region. Gudmundsson and Wolle consider a related problem of computing the passing options that the player in possession has, based on the areas of the pitch that are reachable by the other players [7].…”

Section: Related Workmentioning

confidence: 99%

“…As such, we use an approximation algorithm to compute the dominant region. Taki and Hasegawa [20] and Nakanishi et al [15] both present approximation algorithms where x is approximated by a discrete grid Y ⊂ N 2 , and the dominant regions are thus computed for all points in Y . In the following subsection we present an approximation algorithm that uses the approximate motion model function to compute the boundaries of the dominant regions.…”

Section: The Dominant Regionmentioning

confidence: 99%

Classification of Passes in Football Matches Using Spatiotemporal Data

Chawla

Estephan

Gudmundsson

et al. 2017

ACM Trans. Spatial Algorithms Syst.

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A knowledgeable observer of a game of football (soccer) can make a subjective evaluation of the quality of passes made between players during the game. We investigate the problem of producing an automated system to make the same evaluation of passes. We present a model that constructs numerical predictor variables from spatiotemporal match data using feature functions based on methods from computational geometry, and then learns a classification function from labelled examples of the predictor variables. Furthermore, the learned classifiers are analysed to determine if there is a relationship between the complexity of the algorithm that computed the predictor variable and the importance of the variable to the classifier. 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. Finally, we show that the interrater agreement on pass classification between the machine classifier and a human observer is of similar magnitude to the agreement between two observers. Categories and Subject DescriptorsI.5 [Pattern Recognition]: Design Methodology-Feature evaluation and selection * A poster submission has also been made to the Large Scale Sports Analytics workshop at the KDD conference on this work. This submission covers the problem definition and solution framework described in this paper, and also includes preliminary experimental results on the accuracy of the framework.

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“…There are later studies that further developed more realistic models of calculating the dominant regions. For example, Fujimura and Sugihara [8] and Nakanishi et al [22].…”

Section: Related Resultsmentioning

confidence: 99%

“…However, scattered results are available and in recent years more research is done in the area. See for example Fujimura and Sugihara [8], Grunz et al [10,11], Kim et al [17] and Nakanishi et al [22].…”

Section: Introductionmentioning

confidence: 99%

Football analysis using spatio-temporal tools

Gudmundsson¹,

Wolle²

2014

Computers, Environment and Urban Systems

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Analysing a football match is without doubt an important task for coaches, talent scouts, players and even media; and with current technologies more and more match data is collected. Several companies offer the ability to track the position of the players and the ball with high accuracy and high resolution. They also offer software that include basic analysis tools, for example straight-forward statistics about distance run and number of passes. It is, however, a non-trivial task to perform more advanced analysis. We present a collection of tools that we developed specifically for analysing the performance of football players and teams. The aim, functionality and the underlying algorithms for each tool are presented and discussed.

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An Approximate Computation of the Dominant Region Diagram for the Real-Time Analysis of Group Behaviors

Cited by 12 publications

References 5 publications

Spatio-Temporal Analysis of Team Sports

Spatio-Temporal Analysis of Team Sports

Classification of Passes in Football Matches Using Spatiotemporal Data

Football analysis using spatio-temporal tools

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