跟踪窗口自适应的Mean Shift跟踪

Mean Shift tracking with adaptive tracking window

传统的Mean Shift跟踪算法在目标发生形变时会因跟踪窗不能动态改变尺寸而导致目标跟偏甚至跟丢,因此本文提出了一种新的跟踪窗口大小和方向自适应的改进算法。首先,采用跟踪窗口内协方差矩阵主分量分析法来计算跟踪目标的方向和尺寸大小;然后,联合相似性度量和卡尔曼滤波器来更新跟踪窗口的大小和方向倾角,使之适应目标的变化。实验显示,本算法可对不断旋转和缩放的运动目标进行准确实时跟踪,当目标尺寸在35 pixel×17 pixel到176 pixel×80 pixel之间变化时,平均处理时间为17.45 ms/frame,表明改进的算法能够满足非刚体目标跟踪系统的要求。

A new algorithm to estimate the scale and orientation of a tracking window is presented. The algorithm overcomes the problem that the traditional Mean Shift based tracking algorithm often fails when a deformable target is tracked because of the rigid tracking window. Firstly, the principal components of the variance matrix are adopted to compute the scale and orientation of tracking target, then the similarity measure and Kalman filter are used to update the tracking window. Experimental results show that the algorithm can be implemented in real-time and can adapt to changes of scale and orientation of the moving object. The average computing time is only 17.45 ms/frame with the object＇s scales varying between 35 pixel×17 pixel and 176 pixel× 80 pixel. This algorithm can satisfy the re- quirements for tracking non-rigid objects.