基于Mean shift的筛面物料颗粒目标运动轨迹跟踪

为了获取农业物料在风筛式清选筛面的实际运动规律，通过对多颗粒散体中的目标颗粒进行着色处理，提出采用基于颜色特征向量的Mean shift算法，实现对目标运动轨迹的跟踪。算法根据Bhattacharyya系数的大小判定跟踪目标是否被遮挡，并引入了Kalman滤波器设计。在正常跟踪过程中，利用Kalman滤波器预测每帧Mean shift算法的起始位置，然后利用Mean shift算法对目标进行精确定位，当目标被遮挡时，将其运动视为时不变系统，并通过Kalman滤波器估算目标近似位置。试验结果表明，该方法在复杂背景和光照变化条件下，实现了对快速运动目标的稳定持续跟踪，具有很好的鲁棒性，为散体颗粒运动规律的研究提供了一种图像检测方法。

Trajectory tracking of particle material motion on sieve surface based on Mean shift algorithm

For tracking target particles'motion on air-and-screen cleaning sieve,staining method was used and Mean shift algorithm utilizing the color eigenvector was proposed.Bhattacharyya coefficient was exploited to decide whether the target was occluded.In the frames of regular tracking,the initial point of Mean shift algorithm was predicted by Kalman filter and then the precise position of the target was calculated with Mean shift algorithm.When the occlusion appears,the target motion was regared as a time-invari...