一种改进的基于Camshift的粒子滤波实时目标跟踪算法

为了能够快速和准确地跟踪运动目标，提出了一种改进的基于Camshift的粒子滤波算法。在粒子滤波框架下，首先对传统目标模型进行改进，提出一种新的融合目标颜色信息和运动信息的模型，以增强目标跟踪的稳健性和准确性；同时为了提高跟踪的效率，将一种改进的Camshift算法嵌入到粒子滤波中，用来重新分配随机粒子样本，使之向目标状态的最大后验概率密度方向移动。实验结果表明，与传统的粒子滤波算法或Camshift算法相比，该方法能有效处理目标快速运动或背景存在强干扰等情况，实现对目标快速和稳健的跟踪。

An improved camshift-based particle filter algorithm for real-time target tracking

An improved particle filter algorithm based on Camshift is proposed in order to track the moving target quickly and accarately. Firstly, under the particle filter framework, the algorithm improves the traditional target model and presents a novel target model, which fuses color and motion cues, to enhance the robustness and accuracy of target tracking. Meanwhile, in order to increase the tracking efficiency, an improved Camshift algorithm is embedded into the particle filter to rearrange the random particles, in which the particles moved toward the maximal posterior probability density of the target state. Experimental results show that compared with the traditional particle filter algorithm or Camshift algorithm, the proposed method can successfully cope with the situations of fast moving target or strong disturbances in the background, and achieve fast and robust tracking of the target.