稀疏表示因子模糊粒子滤波的目标追踪

Fuzzy Particle Filter with Sparse Representation Factor for a Tracking Target

为了解决基于颜色直方图粒子滤波等对存在快速变化的目标形状和光照,以及多个相似目标的情况下,目标追踪能力的下降,提出含稀疏因子的模糊粒子滤波方法.该方法综合运用蒙特卡罗算法,模糊测量技术,稀疏因子等方法有效减少了粒子数量,同时增强了粒子滤波的识别定位和实时追踪目标的能力.实验表明,新方法能够在目标形状,姿态以及光照快速变化的序列中实时地,稳定地跟踪目标.该算法增强了粒子滤波对光照快速变化和目标形变的自适应能力,有了较好的鲁棒性和实时性,且部分遮挡的情况下目标不丢失,新的方法或许对目标追踪研究有重要借鉴意义.

Fuzzy particle filter with sparse representation factor in the paper is proposed in order to solve a significant decline in the color histogram-based particle filter things like that tracking in the existence of the rapid changes in the target＇s shape and illumination, as well multiple similar targets. It is more effective in reducing the number of particles, while enhancing the identification target＇s positioning and real-time tracking target by comprehensive use of Monte Carlo algorithm, fuzzy measurement technology, sparse representation factors. Our experimental results shows that It is real-time, stably track the target in sequences of the rapid changes in the target＇s shape and illumination, as well multiple similar targets. It enhances the adaptive ability of the particle filter on the illumination of rapid changes and target deformation, and the partially obscured target is not lost. It is a significant reference for other optimum problem resolved through particle filter on tracking target.