Detection of launch frame in long jump videos using computer vision and discreet computation

Abstract

Pose estimation, a computer vision technique, can be used to develop a quantitative feedback training tool for long jumping. Key performance indicators (KPIs) such as launch velocity would allow a long jumping athlete to optimize their technique while training. However, these KPIs need a prior knowledge of when the athlete jumped, referred to as the launch frame in the context of videos and computer vision. Thus, an algorithm for estimating the launch frame was made using the OpenPose Demo and Matlab. The algorithm estimates the launch frame to within 0.8±0.91 frames. Implementing the algorithm into a training tool would give an athlete real-time, quantitative feedback from a video. This process of developing an algorithm to flag an event can be used in other sports as well, especially with the rise of KPIs in the sports industry (e.g. launch angle and velocity in baseball).