What Are You Looking At?:Team Fight Prediction Through Player Camera

Autor: Marko Tot, Michelangelo Conserva, Alan Pedrassoli Chitayat, Athanasios Kokkinakis, Sagarika Patra, Simon Demediuk, Alvaro Caceres Munoz, Oluseji Olarewaju, Marian Ursu, Ben Kirmann, Jonathan Hook, Florian Block, Anders Drachen, Diego Perez-Liebana
Jazyk: angličtina
Rok vydání: 2021
Předmět:
Zdroj: Tot, M, Conserva, M, Pedrassoli Chitayat, A P, Kokkinakis, A, Patra, S, Demediuk, S, Munoz, A C, Olarewaju, O, Ursu, M F, Kirmann, B, Hook, J, Block, F, Drachen, A & Perez-Liebana, D 2021, What Are You Looking At? Team Fight Prediction Through Player Camera . in 2021 IEEE Conference on Games (CoG) . IEEE, 2021 IEEE Conference on Games, København, Denmark, 17/08/2021 . https://doi.org/10.1109/CoG52621.2021.9619038
DOI: 10.1109/CoG52621.2021.9619038
Popis: Esport is a large and still growing industry with vast audiences. Multiplayer Online Battle Arenas (MOBAs), a sub-genre of esports, possess a very complex environment, which often leads to experts missing important coverage while broadcasting live competitions. One common game event that holds significant importance for broadcasting is referred to as a team fight engagement. Professional player's own knowledge and understanding of the game may provide a solution to this problem. This paper suggests a model that predicts and detects ongoing team fights in a live scenario. This approach outlines a novel technique of deriving representations of a complex game environment by relying on player knowledge. This is done by analysing the positions of the in-game characters and their associated cameras, utilising this data to train a neural network. The proposed model is able to both assist in the production of live esport coverage as well as provide a live, expert-derived, analysis of the game without the need of relying on outside sources.
Databáze: OpenAIRE