Abstract
Sports analytics have become a topic of interest in the field of Artificial intelligence. With the availability of huge volumes of high level data, significant progress has been made in the domain of action recognition in the past. Though video based action recognition has progressed well using state of the art deep learning techniques, its applications are limited to some higher level actions like throwing, jumping, running etc. There has been some work in fine-grained action recognition technique, such as, identification of type of throws in Basketball, and the type of a player’s shots in Tennis. However with larger play field and with many players on field, multi player sports such as Soccer, Rugby, Hockey and etc. pose bigger challenges and remain unexplored. These games in general are live fed through field view cameras or skycams which aren’t stationary. For these reasons, we chose to recognize player’s actions in the game of Soccer and thereby, explore the capabilities of existing architectures and deep neural networks for these kind of games. Our main contributions are the proposed framework that can automatically recognize actions of players in live football game which will be helpful for text query based video search, for extracting stats in a football game and to generate textual commentary and the Soccer-8k dataset which consists of different action clips in the soccer play.
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Ganesh, Y., Sri Teja, A., Munnangi, S.K., Rama Murthy, G. (2019). A Novel Framework for Fine Grained Action Recognition in Soccer. In: Rojas, I., Joya, G., Catala, A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science(), vol 11507. Springer, Cham. https://doi.org/10.1007/978-3-030-20518-8_12
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DOI: https://doi.org/10.1007/978-3-030-20518-8_12
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