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Tracking Sports Players with Context-Conditioned Motion Models

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.

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Tracking Sports Players with Context-Conditioned Motion Models

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  1. Tracking Sports Players with Context-Conditioned Motion Models Jingchen Liu, Peter Carr, Robert T. Collins and YanxiLiu CVPR 2013

  2. Demo

  3. Bayesian Tracking Formulation • Associate detections/observations to trajectories

  4. Kinematic Motion Models • Continuity of motion alone may be insufficient to resolve identity 4 1 3 2 4 2 1

  5. 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

  6. Context-Conditioned Motion Models • Motion models conditioned on the current situation • Context implicitly encodes multi-player interaction

  7. Hierarchical Data Association

  8. Hierarchical Data Association 8 7 1 6 2 4 3 3 5 6 2 1 4 9 8 2 3 1

  9. Hierarchical Data Association • Describe the probability of continuing as • Context features: • Absolute position

  10. Hierarchical Data Association • Describe the probability of continuing as • Context features: • Absolute position • Relative position

  11. Hierarchical Data Association • Describe the probability of continuing as • Context features: • Absolute position • Relative position • Absolute motion

  12. Hierarchical Data Association • Describe the probability of continuing as • Context features: • Absolute position • Relative position • Absolute motion • Relative motion

  13. Context-Conditioned Motion Models • Describe the probability of continuing as • Radom decision forest of 500 trees

  14. Performance

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