为了提高移动机器人目标跟踪系统在复杂环境中的跟踪性能, 提出在双层定位机制下采用基于自适应核函数的Mean Shift算法实现目标跟踪。利用射频识别器件(RFID)检测携带标签的目标, 实现外层粗定位并确定感兴趣区域(ROI); 在内层则根据对视差图的ROI的处理结果确定初始搜索窗口, 然后应用基于自适应核函数的Mean Shift算法在从立体相机获得的左图中应用基于自适应核函数的Mean Shift算法实现对目标的精确定位。自适应核函数由目标的区域特征与Epanechnikov函数相融合构成, 克服了目标边缘处背景像素对目标颜色概率分布的影响。与传统的Mean Shift算法相比, 所提方法在同色背景干扰下仍能准确跟踪目标。另外, RFID限定了图像搜索范围, 节省了运算开支, 图像处理的平均时间为62.11 ms/frame, 满足实时跟踪的要求。实验结果表明, 该方法可实现移动机器人在同色背景干扰、遮挡、目标快速移动等情况下的目标跟踪。

To improve the performance of person tracking for a mobile robot in complex environments, an adaptive kernel function based Mean Shift algorithm was proposed by using a coarse to fine localization mechanism. In the outer layer, a Radio Frequency Identification Device (RFID) was adopted to detect the person with an ID tag to determine the Region of Interest (ROI) coarsely. In the inner layer, the ROI of a disparity image was processed to estimate an initial searching window. Then, the adaptive kernel based Mean Shift algorithm was applied to location of the person precisely in the left image from a stereo camera. The adaptive kernel function was combined with the regional feature of person and the Epanechnikov function, which can reduce the effect of the background pixel on the target’s color probability distribution. Compared with the traditional Mean Shift algorithm, the presented algorithm can track the target successfully when the background has the same color. Furthermore, the searching area is narrowed by the RFID, so that the computational cost is reduced. The average computing time is 62.11 ms/frame, which satisfies the requirements of real-time target tracking. The experimental results indicate that the proposed tracking method can complete the target tracking in a background with the same color, short-term occlusion, fast moving, and a sudden turn for a mobile robot.