针对复杂动态场景中粒子滤波算法跟踪目标时稳定性不高且易受背景噪声影响的特点，提出了改进的卡尔曼粒子滤波（KPF）目标跟踪算法。利用卡尔曼嵌入粒子滤波的方法对粒子滤波预测的状态值进行二次预测，并且利用二次采样技术增强粒子的丰富度，从而在一定程度上消除背景噪声的影响。同时为了满足卡尔曼滤波对线性运动的要求以及消除背景快速变化对跟踪精度的影响，采用灰度投影算法计算背景偏移从而进行运动补偿。实验结果表明，改进的卡尔曼粒子滤波跟踪算法在复杂动态场景中可以有效地跟踪运动目标，证明提出的KPF 算法精度高、稳健性强、实时性好。

Aiming at the unstable characteristics of the particle filter algorithm which is easily affected by background noise in complex dynamic scene while it is tracking target, an improved Kalman particle filter (KPF) target-tracking algorithm is put forward. The method of using embedded Kalman particle filter is used to predict the predicted status value of particle filter secondarily. And the secondary sampling technique is used to enhance particle richness, and thus eliminates the influence of background noise to a certain extent. Besides in order to meet the requirements of Kalman on linear motion and to eliminate the effect of rapid background change on tracking accuracy, the gray projection algorithm is promoted to calculate the background migration for motion compensation. The experiment results show that the improved Kalman particle filter algorithm can effectively track the moving object in the complex dynamic scene, which proves that the propposed KPF algorithm has high precision, strong robustness and good real-time performance.