基于改进Kalman滤波与Camshift结合的嵌入式跟踪系统设计

Design of embedded tracking system based on improved Kalman filter combined with Camshift

针对传统Camshift算法难以在运动目标受遮挡的情况下有效跟踪的问题,提出一种基于改进Kalman滤波与Camshift相结合的目标跟踪算法,通过对Kalman滤波器的状态变量Xk增加高维特征宽高比a与高度h参数,并将P0、Q、R与窗口高度h相关联,同时在受遮挡时自适应改变Xk参数,使Kalman滤波器能够代替Camshift算法输出足够大的跟踪框标注受遮挡目标位置。实验表明,改进算法在有效帧率方面提高了42.6%,且平均BH距离降低了0.27,显著提高了跟踪的准确性和鲁棒性。

Aiming at the problem that traditional Camshift algorithm is difficult to track moving targets effectively when they are obscured,a target tracking algorithm based on the combination of improved Kalman filter and Camshift is proposed.The state variable Xkof Kalman filter is added with high dimensional feature aspect ratio a and height h parameters,the window height h is associated with P0,Q,and R,and the parameter Xkis adaptively changed in the case of occlusion,which make Kalman filter able to replace Camshift algorithm in outputting a sufficiently large tracking box to mark the position of the occluded target.Experimental results show that the effective frame rate of the improved algorithm is increased by 42.6%,and the average BH distance is reduced by 0.27,which significantly improves the tracking accuracy and robustness.