SYSTEM AND METHOD FOR PLAYER REIDENTIFICATION IN BROADCAST VIDEO
A system and method of re-identifying players in a broadcast video feed are provided herein. A computing system retrieves a broadcast video feed for a sporting event. The broadcast video feed includes a plurality of video frames. The computing system generates a plurality of tracks based on the plurality of video frames. Each track includes a plurality of image patches associated with at least one player. Each image patch of the plurality of image patches is a subset of the corresponding frame of the plurality of video frames. For each track, the computing system generates a gallery of image patches. A jersey number of each player is visible in each image patch of the gallery. The computing system matches, via a convolutional autoencoder, tracks across galleries. The computing system measures, via a neural network, a similarity score for each matched track and associates two tracks based on the measured similarity.
1 . A method, comprising:
receiving, by a computing system, a broadcast video feed that includes a plurality of video frames;
classifying, by the computing system, each frame of the plurality of video frames as trackable or untrackable;
modifying, by the computing system, the broadcast video feed by removing a portion of at least one untrackable frame of the plurality of video frames from the broadcast video feed; and
storing, by the computing system, the modified broadcast video feed as a set of trackable frames in a database.
2 . The method of claim 1 , wherein a trained neural network is configured to perform the classifying.
3 . The method of claim 2 , wherein the trained neural network includes an input layer, one or more hidden layers, and an output layer.
4 . The method of claim 1 , wherein the trackable frame corresponds to a frame that includes a captured unified view.
5 . The method of claim 1 , wherein the untrackable frame corresponds to a frame that does not include a captured unified view.
6 . The method of claim 1 , wherein the classifying each frame of the plurality of video frames as trackable or untrackable comprises:
selecting, by the computing system, a frame cluster from the plurality of video frames; and
determining, by the computing system, whether a threshold number of frames of the frame cluster are trackable.
7 . The method of claim 6 , the method further comprising:
in response to determining that the threshold number of frames of the frame cluster are trackable, indicating, by the computing system, that the frame cluster includes trackable frames.
8 . The method of claim 6 , the method further comprising:
in response to determining that the threshold number of frames of the frame cluster is not trackable, indicating, by the computing system, that the frame cluster includes untrackable frames.
9 . A computer system, the computer system comprising:
a memory having processor-readable instructions stored therein; and
one or more processors configured to access the memory and execute the processor-readable instructions, which when executed by the one or more processors configures the one or more processors to perform a plurality of functions, including functions for:
receiving a broadcast video feed that includes a plurality of video frames;
classifying each frame of the plurality of video frames as trackable or untrackable;
modifying the broadcast video feed by removing a portion of at least one untrackable frame of the plurality of video frames from the broadcast video feed; and
storing the modified broadcast video feed as a set of trackable frames in a database.
10 . The computer system of claim 9 , wherein a trained neural network is configured to perform the classifying.
11 . The computer system of claim 9 , wherein the trackable frame corresponds to a frame that includes a captured unified view.
12 . The computer system of claim 9 , wherein the untrackable frame corresponds to a frame that does not include a captured unified view.
13 . The computer system of claim 9 , wherein the classifying each frame of the plurality of video frames as trackable or untrackable comprises:
selecting a frame cluster from the plurality of video frames; and
determining whether a threshold number of frames of the frame cluster are trackable.
14 . The computer system of claim 13 , the functions further comprising:
in response to determining that the threshold number of frames of the frame cluster are trackable, indicating, by the computing system, that the frame cluster includes trackable frames.
15 . The computer system of claim 14 , the functions further comprising:
in response to determining that the threshold number of frames of the frame cluster is not trackable, indicating, by the computing system, that the frame cluster includes untrackable frames.
16 . A non-transitory computer-readable medium containing instructions for generating a player tracking prediction, the instructions comprising:
receiving, by a computing system, a broadcast video feed that includes a plurality of video frames;
classifying, by the computing system, each frame of the plurality of video frames as trackable or untrackable;
modifying, by the computing system, the broadcast video feed by removing a portion of at least one untrackable frame of the plurality of video frames from the broadcast video feed; and
storing, by the computing system, the modified broadcast video feed as a set of trackable frames in a database.
17 . The non-transitory computer-readable medium of claim 16 , wherein a trained neural network is configured to perform the classifying.
18 . The non-transitory computer-readable medium of claim 16 , wherein the classifying each frame of the plurality of video frames as trackable or untrackable comprises:
selecting, by the computing system, a frame cluster from the plurality of video frames; and
determining, by the computing system, whether a threshold number of frames of the frame cluster are trackable.
19 . The non-transitory computer-readable medium of claim 18 , the instructions further comprising:
in response to determining that the threshold number of frames of the frame cluster are trackable, indicating, by the computing system, that the frame cluster includes trackable frames.
20 . The non-transitory computer-readable medium of claim 19 , the instructions further comprising:
in response to determining that the threshold number of frames of the frame cluster is not trackable, indicating, by the computing system, that the frame cluster includes untrackable frames.