Sports video enrichment is attracting many researchers. People want to appreciate some highlight segments with cartoon.
In order to automatically generate these cartoon video, we have to estimate the players' and ball's 3D position. In this
paper, we propose an algorithm to cope with the former problem, i.e. to compute players' position on court. For the
image with sufficient corresponding points, the algorithm uses these points to calibrate the map relationship between
image and playfield plane (called as homography). For the images without enough corresponding points, we use global
motion estimation (GME) and the already calibrated image to compute the images' homographies. Thus, the problem
boils down to estimating global motion. To enhance the performance of global motion estimation, two strategies are
exploited. The first one is removing the moving objects based on adaptive GMM playfield detection, which can
eliminate the influence of non-still object; The second one is using LKT tracking feature points to determine horizontal
and vertical translation, which makes the optimization process for GME avoid being trapped into local minimum. Thus,
if some images of a sequence can be calibrated directly from the intersection points of court line, all images of the
sequence can by calibrated through GME. When we know the homographies between image and playfield, we can
compute the camera focusing area and players' position in real world. We have tested our algorithm on real video and
the result is encouraging.