A Computer Vision Method for Estimating Velocity from Jumps

TL;DR

This paper proposes a computer vision approach to estimate jump velocity from video recordings, providing a low-cost alternative for athletes to assess their lower-limb capacity without specialized equipment.

Contribution

It introduces a novel method for estimating jump velocity using video analysis, demonstrating high accuracy across diverse athletes.

Findings

Velocity estimation achieved an average R-value of 0.71.

The method is applicable to a wide range of athletes.

Video-based assessment can replace traditional equipment.

Abstract

Athletes routinely undergo fitness evaluations to evaluate their training progress. Typically, these evaluations require a trained professional who utilizes specialized equipment like force plates. For the assessment, athletes perform drop and squat jumps, and key variables are measured, e.g. velocity, flight time, and time to stabilization, to name a few. However, amateur athletes may not have access to professionals or equipment that can provide these assessments. Here, we investigate the feasibility of estimating key variables using video recordings. We focus on jump velocity as a starting point because it is highly correlated with other key variables and is important for determining posture and lower-limb capacity. We find that velocity can be estimated with a high degree of precision across a range of athletes, with an average R-value of 0.71 (SD = 0.06).