Human Action Recognition (HAR) is a challenging task used in sports such as volleyball, basketball, soccer, and tennis to detect players and recognize their actions and teams' activities during training, matches, warm-ups, or competitions. HAR aims to detect the person performing the action on an unknown video sequence, determine the action's duration, and identify the action type. The main idea of HAR in sports is to monitor a player's performance, that is, to detect the player, track their movements, recognize the performed action, compare various actions, compare different kinds and skills of acting performances, or make automatic statistical analysis. As an action that can occur in the sports field refers to a set of physical movements performed by a player in order to complete a task using their body or interacting with objects or other persons, actions can be of different complexity. Because of that, a novel systematization of actions based on complexity and level of performance and interactions is proposed. The overview of HAR research focuses on various methods performed on publicly available datasets, including actions of everyday activities. That is just a good starting point; however, HAR is increasingly represented in sports and is becoming more directed towards recognizing similar actions of a particular sports domain. Therefore, this paper presents an overview of HAR applications in sports primarily based on Computer Vision as the main contribution, along with popular publicly available datasets for this purpose.
Keywords: Action systematization; Human Action Recognition; Human action recognition in sports; Machine learning; Sport; Sports dataset.
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