Natural Reference Frames within Video Analysis

TL;DR

This paper proposes using natural reference frames derived from physical principles and data analysis for video motion analysis, offering an alternative to manual alignment in physics experiments.

Contribution

It introduces a method to identify natural reference frames directly from motion data, connecting physical symmetries with data-driven analysis in physics education.

Findings

Natural reference frames can be identified from motion data.

Physical constraints emerge from data without manual alignment.

Applicable to introductory physics experiments.

Abstract

This study explores an alternative approach to video-based motion analysis using natural reference frames rather than relying on manual alignment. We demonstrate how the motion data itself can reveal optimal reference frames, connecting fundamental physical principles with data analysis. We present three classical mechanics experiments: uniform motion along a track, where the motion direction naturally defines the axis; circular motion of a bicycle wheel, where the center of rotation emerges as the reference origin; and projectile motion from a juggling tutorial, where gravity determines the vertical direction. This approach demonstrates how physical constraints and symmetries can naturally emerge from experimental data, providing a viable approach for introductory physics laboratories.