Tracking Sports Players with Context-Conditioned Motion Models

Jingchen Liu, Peter Carr, Robert T. Collins and Yanxi Liu

CVPR 2013

Demo

Bayesian Tracking Formulation

• Associate detections/observations to trajectories

Kinematic Motion Models

• Continuity of motion alone may be insufficient to resolve identity

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Challenges for Tracking Sport Players

• Weak appearance features• Player movements are highly correlated• Current game situation influences how each

individual will move• Independent per-player motion models are

tractable

Context-Conditioned Motion Models

• Motion models conditioned on the current situation• Context implicitly encodes multi-player interaction

Hierarchical Data Association

Hierarchical Data Association

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Hierarchical Data Association

• Describe the probability of continuing as • Context features:– Absolute position

Hierarchical Data Association

• Describe the probability of continuing as • Context features:– Absolute position– Relative position

Hierarchical Data Association

• Describe the probability of continuing as • Context features:– Absolute position– Relative position– Absolute motion

Hierarchical Data Association

• Describe the probability of continuing as • Context features:– Absolute position– Relative position– Absolute motion– Relative motion

Context-Conditioned Motion Models

• Describe the probability of continuing as

• Radom decision forest of 500 trees

Performance