一种快速的单目移动相机下运动目标检测方法

A Fast Method for Moving Target Detection on A Moving Camera

从射影几何的角度分析了单目移动相机下场景运动矢量与摄像机运动之间的关系,基于摄像机光心坐标系,提出了一种快速极线估计算法.该算法中摄像机在此坐标系下永远静止,只有场景和运动目标在运动,将原来移动平台下运动目标检测的问题转换成静止平台下场景全局运动与运动目标独立运动的问题,并推导出光流约束的简洁形式.该算法框架能够根据KLT算法获得Harris角点光流场,并根据实际图像的运动场补偿摄像机的随机运动,同时在保证算法准确性与鲁棒性的前提下,与原来算法相比,计算速度提升了10倍左右.根据实际采集的图像序列进行了分析对比,真实的数据测试表明快速极线估计算法在保证算法准确性与鲁棒性的前提下,极大地降低了算法的计算量与计算时间,从而无需三维重建便可有效地解决单目移动摄像机下运动目标检测的问题.

Based on projective geometry, the relation between the moving camera and still scene was analyzed, and a fast estimation algorithm was proposed based on the optical center coordinate system. On this coordinate in which the camera was always static, the scene and the targets were moving instead. The problem of moving target detection on a moving camera was transformed into distinguishing independent movement of targets from the global motion of scenes on a still platform and a concise form of optical flow constraint was deduced. This framework compensated the random movement according to the optical flow field which was got from KLT. Compared with the original algorithm, the computing speed increased about 10 times while ensuring accuracy and robustness. Experiments were performed based on actual image data, and the results show that this model can reduce the computation time extreamly under the premise of ensuring the accuracy and robustness, and be pratical on moving target detection with a moving camera without construction.