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.

翻译：基于视觉的运动目标运动估计通常被建模为仅方位估计问题，其中视觉测量被表示为方位向量。尽管仅方位方法已研究数十年，但其根本局限在于需要观测器额外的侧向运动来增强目标的可观测性。然而在许多任务中，这种额外侧向运动与观测器的期望运动存在冲突。众所周知，一旦目标在图像中被检测到，就能获得包围目标的边界框。令人惊讶的是，这种常见的视觉测量——特别是其尺寸信息——至今尚未得到充分探索。本文提出一种新的方位-角度方法，通过将图像边界框建模为方位-角度测量来估计目标运动。理论分析与实验结果表明，该方法无需依赖观测器的额外侧向运动即可显著增强可观测性。方位-角度方法的优势无需额外成本，因为边界框是目标检测算法的标准输出。该方法仅利用了以往未被充分利用的信息，不需要额外的传感设备或特殊检测算法。