用于目标跟踪的智能群体优化滤波算法

Swarm intelligence filtering for robust object tracking

针对目标跟踪中的状态估计,提出一种智能群体优化滤波算法。算法在贝叶斯滤波的基础上,运用智能群体优化的3种运动模型估计目标的后验状态,其中内聚运动在保持了粒子多样性的情况下增加了样本的权值,分离运动和排列运动相协调能够更加准确地预测下一时刻目标的先验状态。实验结果表明:与标准粒子滤波相比,该算法能够更加准确地估计非线性系统中的后验状态,在复杂多变的场景环境中,表现出更高的跟踪准确性。

To estimate the state of target in object tracking,a novel algorithm named swarm intelligence filter(SIF)is proposed in this paper.Based on the Bayesian filter,the algorithm could estimate the posterior state using three movements of swarms.The cohesion movement could add the weight by maintaining the diversity of the sample,and the coordination of separation and permutation movements could more accurately predict the state of the next moment compared with the conventional algorithm.The experimental results show that compared with the conventional particle filter,our algorithm could more accurately predict the posterior state in nonlinear systems and more accurately estimate the state of the object in complex environment.