Mimicking Human Camera Operators

Abstract

Filming team sports is challenging because there are many points of interest which are constantly changing. Unlike previous automatic broadcasting solutions, we propose a data-driven approach for determining where a robotic pan-tilt-zoom (PTZ) camera should look. Without using any pre-defined heuristics, we learn the relationship between player locations and corresponding camera configurations by crafting features which can be derived from noisy player tracking data, and employ a new calibration algorithm to estimate the pan-tilt-zoom configuration of a human operated broadcast camera at each video frame. Using this data, we train a regress or to predict the appropriate pan angle for new noisy input tracking data. We demonstrate our system on a high school basketball game. Our experiments show how our data-driven planning approach achieves superior performance to a state-of-the-art algorithm and does indeed mimic a human operator.