改进的均值漂移和粒子滤波混合跟踪方法

Improved object tracking method based on mean shift and particle filter

为提高粒子滤波视觉目标跟踪算法的准确性和实时性,提出一种基于均值漂移和粒子滤波的混合跟踪算法。将相异性较小的粒子进行聚类,利用均值漂移算法迭代各个聚类中的代表点,通过减少参与均值漂移迭代的粒子数来降低运算复杂度;根据跟踪情况自适应调整采样粒子数目和过程噪声分布,以提高跟踪精度和减少运算时间。实验结果表明,所提算法平均每帧计算时间不到传统混合跟踪法的一半,而且跟踪精度也有所提高。

To improve the accuracy and real-time performance of particle filter algorithm for tracking vision object, an improved algorithm in combination with mean shift and particle filter was proposed. Similar particles were clustered, and representative particles were iterated in each cluster by using mean shift algorithm. Then computation complexity was reduced by fewer mean shift iterative particles. Particle number and process noise distribution were adjusted adaptively based on tracking condition to improve tracking accuracy and reduce computation complexity. The experimental results prove the superiority of the proposed method, the average of each frame＇ operation time of this method is less than half of classic bybrid alzorithm, and its comoutation comolexitv is also less than classic bvbrid alzorithm.