简要介绍Kalman滤波跟踪和Mean-shift跟踪并分析其优缺点,在此基础上提出一种融合Kalman预测和Mean-shift搜索的运动目标跟踪新方法。该方法利用Kalman滤波估计出运动目标在下一帧中最可能的出现位置,利用Mean-shift方法据此进行较小范围的搜索和目标匹配,从而可用较小的运算量获得较为可靠的跟踪效果,并适应较复杂的场景。实验结果证明了该算法的有效性。

Following a brief introduction to Kalman-filter-based tracking methods and Mean-shift-based tracking methods and a discussion about their strong points and weak points, a novel approach to tracking of video moving objects (VMOs) is proposed. By using Kalman-filter to predict locations where VMOs most probably appear in a next-frame and Mean-shift algorithm to search in the corresponding areas and match the VMOs, the approach promises to obtain more reliable tracking effect with much less computation cost. The experimental results presented show effectiveness of the approach.赵曙光|sg.zhao@126.com