Abstract: Systems and methods for tracking and trajectory prediction for in-person live action gaming are provided. In one example, video data is captured from multiple cameras monitoring a field of play in which multiple players are engaged in an in-person, live action game. The players and projectiles fired from respective projectile launchers of the players are tracked by applying one or more deep learning algorithms to the video data. Projectile trajectories and potential player impacts by one or more projectiles of the projectiles are predicted. A player of the multiple players is identified as an impacted player by confirming a hit on the player by a projectile fired from a projectile launcher of the respective projectile launchers associated with a shot originator without requiring use of a physical impact sensor on an outfit worn by the player. The hit is then attributed to the shot originator for scoring purposes.

Abstract: Various embodiments provide systems and methods for live action gaming.

Abstract: Various embodiments provide systems and methods for live action gaming. According to one embodiment, a method facilitates scoring for an in-person, live-action game, for example, a projectile-tag scoring game or the like, involving multiple players. A gaming outfit worn by a first player or a projectile launcher of a second player may determine a shot hit by the second player against the first player based at least in part on detection of an impact of a projectile fired by the projectile launcher with the gaming outfit. A score for the in-person, live-action game may then be updated based on the shot hit.