SYSTEMS AND METHODS FOR PLAYER TO TEAM ASSOCIATION BASED ON SPORTS VIDEO FEEDS

1 . A method for associating a player with a team in a sports event, the method comprising:

receiving a video feed of a sporting event;

identifying, based on an output of a first machine learning model, a patch of pixels corresponding to a player in a video frame of the video feed of the sporting event;

determining, based on an output of a second machine learning model, a vector of the patch;

retrieving gallery vectors for each team in the sporting event;

determining a set of distances between the vector and each of the gallery vectors; and

determining, based on a closest distance of the set of distances, a team identification for the player.

2 . The method of claim 1 , wherein the first machine learning model is a convolutional neural network.

3 . The method of claim 1 , wherein the patch of pixels of the player includes a player surrounding area, jersey, shorts, and player features.

4 . The method of claim 1 , wherein the second machine learning model is a classifier.

5 . The method of claim 1 , wherein the vector is normalized to have a score between 0 and 1.

6 . The method of claim 1 , wherein each of the gallery vectors for each team were determined by applying the second machine learning model to patches of pixels representing a player on each team prior to a beginning of the sporting event.

7 . The method of claim 1 , wherein the patch is of a portion of a player representing a jersey of the player.

8 . The method of claim 7 , wherein the method further includes:

identifying, by implementing the first machine learning model, a second patch of pixels of the player in a video frame of the video feed of the sporting event, the second patch of pixels representing a subset of the player;

determining, by implementing the second machine learning model, a second vector of the second patch;

retrieving a second set of gallery vectors for each team in the sporting event;

determining a second set of distances between the vector and each of the gallery vectors;

wherein, determining the team identification of the player includes:

averaging the set of distances and the second set of distances a determining a closest distance average.

9 . The method of claim 1 , wherein determining the set of distances between the vector and each of the gallery vectors includes applying a k-means nearest function to the vector and the gallery vectors.

10 . The method of claim 1 , further including:

determining that the closest distance of the set of distances is under a threshold value to approve the team identification of the player.

11 . The method of claim 1 , wherein the retrieved gallery vectors for each team in the sporting event may be based upon lighting conditions in the from the video frame.

12 . A system for associating a player with a team 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;

identifying, based on an output of a first machine learning model, a patch of pixels corresponding to a player in a video frame of the video feed of the sporting event;

determining, based on an output of a second machine learning model, a vector of the patch;

retrieving gallery vectors for each team in the sporting event;

determining a set of distances between the vector and each of the gallery vectors; and

determining, based on a closest distance of the set of distances, a team identification for the player.

13 . The system of claim 12 , wherein the first machine learning model is a convolutional neural network.

14 . The system of claim 12 , wherein the patch of pixels of the player includes a player surrounding area, jersey, shorts, and player features.

15 . The system of claim 12 , wherein the second machine learning model is a classifier.

16 . The system of claim 12 , wherein the vector is normalized to have a score between 0 and 1.

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;

identifying, based on an output of a first machine learning model, a patch of pixels corresponding to a player in a video frame of the video feed of the sporting event;

determining, based on an output of a second machine learning model, a vector of the patch;

retrieving gallery vectors for each team in the sporting event;

determining a set of distances between the vector and each of the gallery vectors; and

determining, based on a closest distance of the set of distances, a team identification for the player.

18 . The non-transitory computer readable medium of claim 17 , wherein the first machine learning model is a convolutional neural network.

19 . The non-transitory computer readable medium of claim 17 , wherein the patch of pixels of the player includes a player surrounding area, jersey, shorts, and player features.

20 . The non-transitory computer readable medium of claim 17 , wherein the second machine learning model is a classifier.