Start-end time detection in baseball videos for automatic pitching trajectory analysis

Abstract

A baseball pitching trajectory analysis system is used to train pitchers and provide baseball content for viewers. Previous systems needed a system manager because the trajectory was detected manually, thus system automation is essential for popularizing baseball pitching trajectory analysis systems, and detecting the pitching start and end times comprise the key point toward achieving this. In this paper, we define the pitching start and end times according to the situations in baseball games and accurately detect these points in time based on a physical model. Experiments on image data captured at a professional baseball game indicate that the proposed method could detect the start and end times of a pitch as accurately as a human can.