Abstract
The popularity of modern competitive gaming (or e-sports) has skyrocketed in the past decade. A key part of e-sports is the spectating experience where fans watch tournament games through a camera of the observer. Bigger tournaments hire professional human observers with high-end tools to monitor important events in the game map for broadcasting the game. This setup is prone to errors. It results in missing important events within the game and lowers the spectating experience overall. It is also not sustainable in long-term and not affordable for the small-scale tournaments. This paper proposes a novel method of automated camera movement control using the AdaRank learning-to-rank algorithm to find and predict important events so the camera can be focused on time. The Dota 2 game setup and its replay data are used in extensive experimental testing. The proposed method has shown to outperform the accuracy of both a past machine learning approach and a professional team of human observers.
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Lie, H., Lukas, D., Liebig, J., Nayak, R. (2019). A Novel Learning-to-Rank Method for Automated Camera Movement Control in E-Sports Spectating. In: Islam, R., et al. Data Mining. AusDM 2018. Communications in Computer and Information Science, vol 996. Springer, Singapore. https://doi.org/10.1007/978-981-13-6661-1_12
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DOI: https://doi.org/10.1007/978-981-13-6661-1_12
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