SYSTEMS AND METHODS FOR RECURRENT GRAPH NEURAL NET-BASED PLAYER ROLE IDENTIFICATION
A method for identifying a player in a sports event, the method including: receiving a video feed of a sporting event; capturing, by a computing system, positional data of a player in one or more video frames of the video feed; receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player; determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
1 . A method for identifying a player in a sports event, the method comprising:
receiving a video feed of a sporting event;
capturing, by a computing system, positional data of a player in one or more video frames of the video feed;
receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player;
determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and
generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
2 . The method of claim 1 , wherein capturing position data of a player in one or more video frames of the video feed includes:
assigning an x, y coordinate values to the player relative to a field of the sporting event; and
assigning a time stamp to the x, y coordinate values.
3 . The method of claim 1 , wherein generating the player identification for the player further includes:
receiving a set of jersey numbers for each player in the sporting event;
identifying an associated number with the player from the video feed, the associated number having an assigned confidence value;
determining that the associated number matches an assigned jersey number from the set of jersey numbers; and
determining that the confidence value is above a threshold value.
4 . The method of claim 1 , wherein generating the player identification for the player further includes:
receiving a set of facial construction data for each player in the sporting event;
identifying data of a facial reconstruction of the player from the video feed; and
determining that the facial reconstruction of the player matches facial construction data received for the player.
5 . The method of claim 1 , wherein the team formation data includes a positional setup of players on a field of the sporting event.
6 . The method of claim 1 , wherein the team formation data is generated, by the computing system, based on exemplary data captured prior to a start of the sports event.
7 . The method of claim 1 , further including:
retrieving event data related to the sporting event, and
based upon the retrieved event data, updating the team formation data dynamically.
8 . The method of claim 7 , wherein updating the team formation data includes:
updating a formation for the at least one team or a listing of players in the sporting event.
9 . The method of claim 1 , wherein correspondence between the positional data of the player and the team formation data is determined by a graph recurrent neural network.
10 . A system for identifying a player in a sports event, the system comprising:
a memory configured to store processor-readable instructions; and
a processor operatively connected to the memory, and configured to execute the instructions to perform operations comprising:
receiving a video feed of a sporting event;
capturing, by a computing system, positional data of a player in one or more video frames of the video feed;
receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player;
determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and
generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
11 . The system of claim 10 , wherein capturing position data of a player in one or more video frames of the video feed includes:
assigning an x, y coordinate values to the player relative to a field of the sporting event; and
assigning a time stamp to the x, y coordinate values.
12 . The system of claim 11 , wherein generating the player identification for the player further includes:
receiving a set of jersey numbers for each player in the sporting event;
identifying an associated number with the player from the video feed, the associated number having an assigned confidence value;
determining that the associated number matches an assigned jersey number from the set of jersey numbers; and
determining that the confidence value is above a threshold value.
13 . The system of claim 10 , wherein generating the player identification for the player further includes:
receiving a set of facial construction data for each player in the sporting event;
identifying data of a facial reconstruction of the player from the video feed; and
determining that the facial reconstruction of the player matches facial construction data received for the player.
14 . The system of claim 10 , wherein the team formation data includes a positional setup of players on a field of the sporting event.
15 . The system of claim 10 , wherein the team formation data is generated, by the computing system, based on exemplary data captured prior to a start of the sports event.
16 . The system of claim 10 , wherein the operations further comprise:
retrieving event data related to the sporting event, and
based upon the retrieved event data, updating the team formation data dynamically.
17 . A non-transitory computer readable medium configured to store processor-readable instructions, wherein when executed by a processor, the instructions perform operations comprising:
receiving a video feed of a sporting event;
capturing, by a computing system, positional data of a player in one or more video frames of the video feed;
receiving, by the computing system, team formation data for at least one team in the sporting event, wherein the team formation data comprises a player role associated with each player;
determining, by the computing system, a correspondence between the positional data of a player and the team formation data; and
generating, by the computing system, a player identification for the player, wherein the player identification is based on the correspondence between the positional data for the player and a player role from the team formation data.
18 . The non-transitory computer readable medium of claim 17 , wherein capturing position data of a player in one or more video frames of the video feed includes:
assigning an x, y coordinate values to the player relative to a field of the sporting event; and
assigning a time stamp to the x, y coordinate values.
19 . The non-transitory computer readable medium of claim 17 , wherein generating the player identification for the player further includes:
receiving a set of jersey numbers for each player in the sporting event;
identifying an associated number with the player from the video feed, the associated number having an assigned confidence value;
determining that the associated number matches an assigned jersey number from the set of jersey numbers; and
determining that the confidence value is above a threshold value.
20 . The non-transitory computer readable medium of claim 17 , wherein generating the player identification for the player further includes:
receiving a set of facial construction data for each player in the sporting event;
identifying data of a facial reconstruction of the player from the video feed; and
determining that the facial reconstruction of the player matches facial construction data received for the player.