Download Computer Vision in Sports by Adrian Hilton, Graham Thomas, Thomas B. Moeslund PDF

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By Adrian Hilton, Graham Thomas, Thomas B. Moeslund

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11. 3 Experiments In this section, we first introduce data sets used in our evaluation and tracking algorithms under comparison. We then define performance metrics. Finally, experimental results are presented, including a study of sensitivity of the proposed method to parameters. 38 F. Yan et al. Fig. 10 Ball trajectories (after interpolation and key event detection) superimposed on mosaic images. From (a) to (c): the first, second and third play in time order. The first and second plays overlap in time: the first play spans frame 16–260; the second frame 254–321; and the third frame 427–887 Fig.

Yan et al. Intuitively, a good path is one that is both “light” and “long”. However, there is usually a trade-off between the weight and the length of a path: a longer path tends to be heavier. 14) ⎩ ⎭ ⎩ ⎭ > < where the relation operators “>”, “=” and “<” between P1 and P2 stand for “is better than”, “has the same quality as”, and “is worse than”, respectively; W1 and W2 are the weights of P1 and P2 , respectively; L 1 and L 2 are the lengths of P1 and P2 , respectively; and α is a controllable parameter with a unit of pixel.

2–4. Fully automated player tracking and pose estimation is difficult, but if solved would open the door to applications such as automated camera control and in-depth analysis of sports such as athletics, as well as better generation of statistics or synthesis of virtual views; some new approaches for this will be explored in Chaps. 5–8. An area that is currently not well covered by commercial products is the ‘higher level’ analysis of sports, where multiple event or motion detections are used to infer things like the type of sport being played, or to detect kinds of events within a sport, for example for automatic highlights generation or searching of video archives.

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