  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 object projection feature estimation techniques are based on
ray-casting from the inner point. These techniques present three main
drawbacks: when the inner point is surrounded by edges, rays may not reach
other relevant areas; as a consequence of that issue, the estimated features
may greatly vary depending on the position of the inner point relative to the
object projection; and finally, increasing the number of rays being casted and
the ray-casting iterations (which would make the results more accurate and
stable) increases the processing time to the point the tracking cannot be
performed on the fly. In this paper, we analyze an intuitive filling-based
object projection feature estimation technique that solves the aforementioned
problems but is too sensitive to edge miscalculations. Then, we propose a less
computing-intensive modification to that technique that would not be affected
by the existing techniques issues and would be no more sensitive to edge
miscalculations than ray-casting-based techniques.
