avtomatska analiza gibanja v izbranih moštvenih športnih igrah

activity templates, stored in a database. To do this we have to solve the problem
of player-role correspondence between the player roles in the analyzed activity
and the activity template. For this reason we decompose the compared semantic
description into five distinctive player agendas. By comparing the individual
player agendas we solve the problem of player correspondence and at the same
time obtain the similarity of the compared semantic description. Finally, the
analyzed activity is assigned the label of the template that obtained the highest
similarity (smallest distance of descriptions). Such an approach has turned out
to be extremely robust with regard to the spurious symbols in the descriptions.
The spurious symbols are obtained as a consequence of incomplete segmentation
and errors in the data acquisition procedures.
Once the activity is recognized and the players-role correspondence is
established, we can perform a detailed analysis of the activity by precisely
analyzing different temporal and logical relations between individual actions. To
do this we propose two different approaches for modeling the temporal and logical
relations between actions. In the first case a multi-level Bayesian network is
used to model the relations between different levels of abstraction which can be
observed in the activity (i.e. activity, player, individual actions and temporal
relations). In the second case the Petri net is used for evaluation. In this case
the individual actions are modeled as instantaneous events in the form of a three-
node action chains which are connected together by considering the previously
described relations. The evaluation of the spatial and temporal properties is in
both cases carried out using trajectory-based action detectors. The two presented
approaches were evaluated using several examples of real basketball activities.
The obtained experimental results suggest that these approaches can be used for
the activity evaluation as well as to determine the stage in which the activity was
concluded. Additionally, we can conclude that some complementary information
should be used in order to bridge the gap between the scores obtained by the
experts and the scores obtained by the proposed evaluation methods.
Described analysis methods were built into a prototypical game analysis
module which is a minor part of a larger analysis system that was developed
in recent years. The game analysis module enables the user to perform the
qualitative analysis of the game using the previously described analysis methods.
Furthermore, several additional functions were developed which enable the user
to obtain the quantitative data about the player motion during the game. The