SYSTEM AND METHOD FOR GENERATING TRACKABLE VIDEO FRAMES FROM BROADCAST VIDEO

1 . A method of generating trackable frames from a broadcast video feed, comprising:

retrieving, by a computing system, a broadcast video feed for a sporting event, the broadcast video feed comprising a plurality of video frames;

generating, by the computing system, a set of frames for classification, wherein the set of frames are a subset of the plurality of video frames;

partitioning, by the computing system, each frame of the set of frames into a plurality of clusters;

classifying, by the computing system, each frame of the plurality of frames as trackable or untrackable;

comparing, by the computing system, each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames; and

performing a camera calibration on at least a subset of clusters that are determine to comprise at least the threshold number of trackable frames.

2 . The method of claim 1 , wherein generating, by the computing system, the set of frames for classification comprises:

extracting, from the plurality of video frames, a frame every selected time-interval to generate a model of the broadcast video feed;

identifying, via the model, a subset of frames from the extracted frames; and

using the subset of frames as the set of frames for classification.

3 . The method of claim 2 , wherein partitioning, by the computing system, each frame of the set of frames into the plurality of clusters comprises:

labeling each frame in the subset of frames with a respective cluster number.

4 . The method of claim 3 , wherein classifying, by the computing system, each frame of the plurality of frames as trackable or untrackable comprises:

training a neural network to classify a video frame as trackable or untrackable using a training set comprising a plurality of video frames and a label associated with each video frame, wherein the label is trackable or untrackable.

5 . The method of claim 4 , wherein each frame of the plurality of frames comprises a trackable/untrackable classification and an associated cluster number.

6 . The method of claim 5 , wherein comparing, by the computing system, each cluster to the predetermined threshold to determine whether each cluster comprises at least the threshold number of trackable frames comprises:

identifying each frame corresponding to a given cluster label; and

determining a number of frames corresponding to the given cluster label that include a trackable classification.

7 . The method of claim 1 , further comprising:

storing each cluster that includes at least the threshold number of trackable frames in a data store.

8 . A system for re-identifying players in a broadcast video feed, comprising:

a processor; and

a memory having programming instructions stored thereon, which, when executed by the processor, performs one or more operations, comprising:

retrieving a broadcast video feed for a sporting event, the broadcast video feed comprising a plurality of video frames;

generating a set of frames for classification, wherein the set of frames are a subset of the plurality of video frames;

partitioning each frame of the set of frames into a plurality of clusters;

classifying each frame of the plurality of frames as trackable or untrackable; and

comparing each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames; and

performing a camera calibration on at least a subset of clusters that are determine to comprise at least the threshold number of trackable frames.

9 . The system of claim 8 wherein generating the set of frames for classification comprises:

extracting, from the plurality of video frames, a frame every selected time-interval to generate a model of the broadcast video feed;

identifying, via the model, a subset of frames from the extracted frames; and

using the subset of frames as the set of frames for classification.

10 . The system of claim 9 , wherein partitioning each frame of the set of frames into the plurality of clusters comprises:

labeling each frame in the subset of frames with a respective cluster number.

11 . The system of claim 10 , wherein classifying each frame of the plurality of frames as trackable or untrackable comprises:

training a neural network to classify a video frame as trackable or untrackable using a training set comprising a plurality of video frames and a label associated with each video frame, wherein the label is trackable or untrackable.

12 . The system of claim 11 , wherein each frame of the plurality of frames comprises a trackable/untrackable classification and an associated cluster number.

13 . The system of claim 12 , wherein comparing each cluster to the predetermined threshold to determine whether each cluster comprises at least the threshold number of trackable frames comprises:

identifying each frame corresponding to a given cluster label; and

determining a number of frames corresponding to the given cluster label that include a trackable classification.

14 . The system of claim 13 , further comprising:

storing each cluster that includes at least the threshold number of trackable frames in a data store.

15 . A non-transitory computer readable medium including one or more sequences of instructions that, when executed by one or more processors, perform one or more operations comprising:

retrieving, by a computing system, a broadcast video feed for a sporting event, the broadcast video feed comprising a plurality of video frames;

generating, by the computing system, a set of frames for classification, wherein the set of frames are a subset of the plurality of video frames;

partitioning, by the computing system, each frame of the set of frames into a plurality of clusters;

classifying, by the computing system, each frame of the plurality of frames as trackable or untrackable; and

comparing, by the computing system, each cluster to a predetermined threshold to determine whether each cluster comprises at least a threshold number of trackable frames; and

performing a camera calibration on at least a subset of clusters that are determine to comprise at least the threshold number of trackable frames.

16 . The non-transitory computer readable medium of claim 15 , wherein generating, by the computing system, the set of frames for classification comprises:

extracting, from the plurality of video frames, a frame every selected time-interval to generate a model of the broadcast video feed;

identifying, via the model, a subset of frames from the extracted frames; and

using the subset of frames as the set of frames for classification.

17 . The non-transitory computer readable medium of claim 16 , wherein partitioning, by the computing system, each frame of the set of frames into the plurality of clusters comprises:

labeling each frame in the subset of frames with a respective cluster number.

18 . The non-transitory computer readable medium of claim 17 , wherein classifying, by the computing system, each frame of the plurality of frames as trackable or untrackable comprises:

training a neural network to classify a video frame as trackable or untrackable using a training set comprising a plurality of video frames and a label associated with each video frame, wherein the label is trackable or untrackable.

19 . The non-transitory computer readable medium of claim 18 , wherein each frame of the plurality of frames comprises a trackable/untrackable classification and an associated cluster number.

20 . The non-transitory computer readable medium of claim 19 , wherein comparing, by the computing system, each cluster to the predetermined threshold to determine whether each cluster comprises at least the threshold number of trackable frames comprises:

identifying each frame corresponding to a given cluster label; and

determining a number of frames corresponding to the given cluster label that include a trackable classification.