1. Tracking Sports Players with Context- Conditioned Motion Models Jingchen Liu, Peter Carr, Robert T. Collins and Yanxi Liu 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 1 2 3 4 1 2 4

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. 1 2 3 4 1 2 4 3 1 2 3 6 7 8 5 6 8 9

9. 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