中文摘要：

针对Kalman预测跟踪和K-近邻数据关联算法的优缺点，研究一种基于Kalman预测和K-近邻的多目标跟踪方法。该方法首先利用Kalman滤波预测出运动目标在下一帧中最可能出现的位置，接着根据当前帧目标位置和预测目标位置的距离，确定搜索半径，利用K-近邻数据关联算法，在该半径范围内，计算与预测点欧式距离最短的目标，并将其确定为真实目标位置。在MATLAB仿真环境下实现该跟踪算法，实验结果表明，用该方法进行多目标跟踪时，跟踪效果和性能较为稳定和可靠。此外选择合理的K值，能减少运算量，加快系统处理速度。

英文摘要：

In this paper, directing at the strong points and weak points of Kalman filter based on tracking method, a novel approach to tracking of muti-objects is studied. By using Kalman-filter, the researchers predict locations where objects most probably appear in a next-frame, determine the search radius and k, calculate the shortest Euclidean distance with the predicted target object in this radius by using k-nearest neighbor, and then determine the true target location. Based on the MATLAB simulation environment to achieve the tracking algorithm, experimental results show that tracking results and performance is better. In addition, selecting a reasonable k values, can reduce the computation and speed up the system processing speed.