基于均值移动与粒子滤波融合的跟踪算法

Tracking Algorithm Based on Fusion of Mean-shift and Particle Filtering

针对均值移动算法鲁棒性差以及粒子滤波算法计算量大、难以满足实时跟踪的特点,提出2种先均值移动后粒子滤波的融合算法,分别为粒子数目保持恒定的融合算法和粒子数目自适应的融合算法。实验结果证明,与已有算法相比,2种算法在实时性提高的同时,跟踪准确性和抗干扰能力没有明显下降。

This paper proposes two fusion algorithms based on poor robustness of Mean-shift algorithm and large computation of particle filtering algorithm. Both of the new algorithms take Mean-shift first and particle filtering later. The number of particles is constant and the number of particles is adaptive. Experimental results show that compared with existing algorithms, the real-time of new algorithms is improved, while tracking accuracy and anti-interference ability without significant decline.