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

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