  We present an online SLAM system specifically designed to track pan-tilt-zoom
(PTZ) cameras in highly dynamic sports such as basketball and soccer games. In
these games, PTZ cameras rotate very fast and players cover large image areas.
To overcome these challenges, we propose to use a novel camera model for
tracking and to use rays as landmarks in mapping. Rays overcome the missing
depth in pure-rotation cameras. We also develop an online pan-tilt forest for
mapping and introduce moving objects (players) detection to mitigate negative
impacts from foreground objects. We test our method on both synthetic and real
datasets. The experimental results show the superior performance of our method
over previous methods for online PTZ camera pose estimation.
