  Vision-based estimation of the motion of a moving target is usually
formulated as a bearing-only estimation problem where the visual measurement is
modeled as a bearing vector. Although the bearing-only approach has been
studied for decades, a fundamental limitation of this approach is that it
requires extra lateral motion of the observer to enhance the target's
observability. Unfortunately, the extra lateral motion conflicts with the
desired motion of the observer in many tasks. It is well-known that, once a
target has been detected in an image, a bounding box that surrounds the target
can be obtained. Surprisingly, this common visual measurement especially its
size information has not been well explored up to now. In this paper, we
propose a new bearing-angle approach to estimate the motion of a target by
modeling its image bounding box as bearing-angle measurements. Both theoretical
analysis and experimental results show that this approach can significantly
enhance the observability without relying on additional lateral motion of the
observer. The benefit of the bearing-angle approach comes with no additional
cost because a bounding box is a standard output of object detection
algorithms. The approach simply exploits the information that has not been
fully exploited in the past. No additional sensing devices or special detection
algorithms are required.
