Using Barriers to Reduce the Sensitivity to Edge Miscalculations of Casting-Based Object Projection Feature Estimation

TL;DR

This paper introduces a barrier extension to casting-based object projection feature estimation methods, enhancing robustness against edge miscalculations caused by low contrast or motion blur in 3D motion tracking.

Contribution

The paper proposes a novel barrier extension to existing casting-based techniques to reduce sensitivity to edge miscalculations in markerless 3D motion tracking.

Findings

Improved robustness in feature estimation under challenging conditions

Enhanced accuracy in 3D motion tracking with edge inaccuracies

Demonstrated effectiveness through experimental validation

Abstract

3D motion tracking is a critical task in many computer vision applications. Unsupervised markerless 3D motion tracking systems determine the most relevant object in the screen and then track it by continuously estimating its projection features (center and area) from the edge image and a point inside the relevant object projection (namely, inner point), until the tracking fails. Existing reliable object projection feature estimation techniques are based on ray-casting or grid-filling from the inner point. These techniques assume the edge image to be accurate. However, in real case scenarios, edge miscalculations may arise from low contrast between the target object and its surroundings or motion blur caused by low frame rates or fast moving target objects. In this paper, we propose a barrier extension to casting-based techniques that mitigates the effect of edge miscalculations.