跟踪框自适应的尺度变化目标跟踪算法

为解决尺度变化的目标跟踪问题,借助于对数极坐标变换良好的尺度旋转不变性,提出一种基于椭圆对数极坐标变换域下目标跟踪算法。算法利用一种显著性加权的Mean Shift进行空间定位,进而将目标区域变换到椭圆对数极坐标系下并沿尺度轴进行积分,通过一维的最大相关匹配确定目标的尺度参数。实验结果表明,该算法不仅空间跟踪误差较低,而且能够较稳定地适应目标尺度变化,具有较好的鲁棒性。

Algorithm of scale change objects tracking with adaptive bandwidth

Taking advantage of the invariant characters for translation and rotation of the Log-polar transformation, a scale tracking method based on the ellipse log-polar transform was investigated to resolve the problem of scale change object tracking. Firstly, a saliency weighted mean shift was presented to locate the objects spatial orientation, then the candidate object region was translated to the ellipse log-polar transformation and integrated along the scale axis. The new proposed method estimated the targets scale parameters according to the maximum correlation coefficient in the transform domain. Experimental results demonstrated that the algorithm could adapt to the objects scale changes and the tracking error was lower. Compared with the traditional, it had a better robustness.