Pan-tilt-zoom SLAM for Sports Videos
This addresses the challenge of tracking PTZ cameras in highly dynamic sports environments, which is an incremental improvement for sports video analysis.
The paper tackled the problem of online SLAM for pan-tilt-zoom cameras in fast-paced sports like basketball and soccer, achieving superior performance in camera pose estimation compared to previous methods.
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.