基于改进重采样的粒子滤波红外车辆跟踪算法

Particle Filter Infrared Vehicle Tracking Algorithm Based on Improved Resampling

针对传统粒子滤波算法状态方程无法利用多帧信息及重采样阶段的粒子种类缺失问题,提出基于改进重采样的粒子滤波红外车辆跟踪算法,对图像进行预处理,增强图像;引入图像准则并结合多帧信息对粒子滤波的状态方程予以改良,在保证以目标帧间变化为基础的前提下将图像信息更多的结合在状态方程中,提高算法的抗干扰能力;在重采样阶段利用粒子权值设定阈值,在保留原始大权重粒子的基础上引入受小权重粒子影响的新粒子,抑制粒子权重过于集中,保证粒子的多样性。经过实验验证,提出的算法在精确性与抗干扰性方面与传统粒子滤波方法相比有较大提升。

The state equation can’t use the multi-frame information and the particle species in resampling stage are lack in the traditional particle filter algorithm. In view of these problems,a particle filter algorithm based on improved resampling is proposed in the detection and tracking stage. Firstly,the image is preprocessed and enhanced. Then the image criterion is introduced and the state equation of particle filter is improved by combining multi-frame information. As a prerequisite for ensuring the change of target frames, the image information is more combined in the state equation to improve the anti-interference capability of the algorithm. In the resampling stage,the threshold value is set by the particle weight,and the new particle affected by the small weight particle is introduced based on reserving the large weight particle,which can not only restrain the excessive concentration of particle weight,but also ensure the diversity of particles. The experimental results show that the proposed algorithm is more accurate,and has improved anti-interference capability compared with the traditional particle filter.