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

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

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