avtomatska analiza gibanja v izbranih moštvenih športnih igrah
avtomatska analiza gibanja v izbranih moštvenih športnih igrah 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
developed system is a result of collaboration with different sport experts and was already successfully tested in several domestic and international studies. Key words: computer vision, probabilistic models, multi-agent motion analysis, trajectory data, Bayesian networks, Petri nets, Gaussian Mixture models, Support Vector Machines, game segmentation, basketball, handball
- Page 1: Univerza v Ljubljani Fakulteta za e
- Page 5: Zahvala ”Čeprav si igral tekmo s
- Page 8 and 9: Na podlagi segmentacije dobimo mno
- Page 11: Abstract This thesis is focused on
- Page 16 and 17: 3.2.1 Klasifikacija posameznih vzor
- Page 18 and 19: ˇZivljenjepis 145 Bibliografija 14
- Page 20 and 21: 2 Uvod Na vseh naˇstetih področji
- Page 22 and 23: 4 Uvod kar M n različnih stanj. V
- Page 24 and 25: 6 Uvod 1.1.1 Hierarhična analiza s
- Page 26 and 27: 8 Uvod mnoˇzico ˇsablon, ki so sh
- Page 28 and 29: 10 Uvod člankov lahko ugotovimo, d
- Page 30 and 31: 12 Uvod in hitrosti gibanja, ki sta
- Page 32 and 33: 14 Uvod pa je ponazorjeno s prehodi
- Page 34 and 35: 16 Uvod akcij) uporabljena dognanja
- Page 36 and 37: 18 Struktura moˇstvenih iger in pr
- Page 38 and 39: 20 Struktura moˇstvenih iger in pr
- Page 40 and 41: 22 Struktura moˇstvenih iger in pr
- Page 42 and 43: 24 Struktura moˇstvenih iger in pr
- Page 44 and 45: Poglavje 3 Časovna segmentacija mo
- Page 46 and 47: 28 Časovna segmentacija moˇstveni
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developed system is a result of collaboration with different sport experts and was<br />
already successfully tested in several domestic and international studies.<br />
Key words: computer vision, probabilistic models, multi-agent motion<br />
analysis, trajectory data, Bayesian networks, Petri nets, Gaussian Mixture<br />
models, Support Vector Machines, game segmentation, basketball, handball
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