avtomatska analiza gibanja v izbranih moštvenih športnih igrah

Abstract
This thesis is focused on the automatic multi-agent trajectory-based analysis of
player motion in team sports. The hierarchical structure of the sport games is
considered when developing the analysis methods. This way we decompose a
very complex problem of team sport analysis into several subproblems. Given
the hierarchical structure of the team sports, we propose a three-step analysis
procedure comprised of segmentation of continuous game into several game
phases, activity recognition inside the individual game phases and activity
evaluation using activity templates. The proposed methods have been tested
in basketball and partially in handball since, from the analysis standpoint, this
two games represent a very challenging multi-agent environment.
In the process of the game segmentation we try to find parts of the game that
are particularly interesting for further analysis (e.g. offensive phase or defensive
phase of the game). To do this we propose a two-stage segmentation procedure. In
the first stage a probabilistic game model is applied to the player-trajectory data
in order to segment individual game instants into one of the three game phases
(offensive game, defensive game and time-outs). In the second stage a nonlinear
or Gaussian smoothing kernel is used to enforce the temporal continuity of the
game. The obtained results suggest that the proposed approach is very robust
and as such could be applied to various team sports. It can handle unusual game
situations such as player exclusions, substitution or injuries which may happen
during the game.
Once the game is segmented into the game phases we obtain a set of short game
segments which denote the offensive or defensive team activities. For this reason,
the next step of the analysis procedure focuses on the recognition of the type of
activity the team was performing. To do this, we propose the use of trajectory-
based action detectors (e.g. starting formation, screen and move) which are
the building blocks of basketball game. This detectors are used to produce a
semantic description of the observed activity. Finally, the activity is recognized
by comparing its semantic description with the descriptions of manually defined