1. http://vision.fe.uni-lj.si/ Janez Perš 1, Marta Bon 2, Stanislav Kovačič 1 1 Faculty of Electrical Engineering, University of Ljubljana, Slovenia 1 Faculty of Sport, University of Ljubljana, Slovenia Sixth Computer Vision Winter Workshop, Bled, Slovenija, February 7-9 2001. janez.pers@fe.uni-lj.si, marta.bon@sp.uni-lj.si, stanek@fe.uni-lj.si Errors and Mistakes in Automated Player Tracking

2. Outline Introduction and motivation Sources and types of errors Tracking parameters Ground truth, experiments and results Conclusion

3. Introduction and motivation Automated player tracking system has been developed in our lab. How accurate is it?

4. Introduction and motivation Related work: 10 articles describing people/sport related tracking at ICPR 2000, Barcelona. 8 did not mention tracking accuracy 1 proposed the accuracy measure 1 specified the accuracy (tracking the tennis ball) Research is focused mainly on reliability. Our motivation: sport experts (end users).

5. Sources and types of errors Note: We track global movement of players for the purpose of match analysis. No operator mistakes are allowed.

6. Tracking parameters Tracking method used: 3 available 2 included in experiments Post-processing of trajectories filter width Position of the players: radial distortion non-uniform quantization Activity of the players: movement of extremities

7. Ground truth A reliable reference for player position was needed. Several patterns were drawn on the court, players were instructed to follow them. Patterns were measured using measuring tape.

8. Experiment I. Position accuracy (RMS error) and path length error with respect to: different tracking methods player position player activity. 2 players at court boundary, 3 near court center. 60 seconds (@25 fps) standing still, 150 seconds active (passing ball, jumping - all at the same position).

9. Experiment I. 2D histograms of player position,cumulative histograms of absolute position error: Combined (color + template tracking) method performed better than background subtraction. Wide FIR filter decreases error in path length - heavy smoothing preferred. RMS position error: 0.18 - 0.50 m (still) 0.28 - 0.63 m (active) Path length error: 0.6 - 1 m/player/min (still) 6 - 10 m/player/min (active)

10. Experiment II. Effect of FIR filter width (wide filter = intense smoothing) to square trajectory. Error measure: In presence of rapid changes in player direction, wide filter distorts trajectory.

11. Experiment III. Velocity accuracy (RMS error) for uniform player motion. Circular trajectory as ground truth, constant velocity of players assumed. Reference velocity calculated from trajectory length and the time player needed for one round. 5 players, Vref from 2.7 to 3.2 m/s RMS error of player velocity: less smoothing: 0.21 to 0.35 m/s (6%-12%) more smoothing: 0.07 to 0.20 m/s (3%-7%)

12. Experiment IV. Velocity and position accuracy. APAS (manual video-based kinematic analysis tool) was used as a ground truth. Two short sequences only - motion analysis using APAS is time consuming. RMS position error: 0.28 m to 0.38 m RMS velocity error: 0.5 m/s to 0.7 m/s

13. Conclusion RMS position error: from 0.2 m to 0.6 m RMS velocity error: from 0.2 m/s to 0.4 m/s Path length error: from +1 m to +10 m (per one player per minute) Players are large non-rigid objects - at this scale the limit is imposed by the definition of player position, velocity and path length itself. Ideas for future work: above definitions and similar research concerning manual position measurements.|