CAJ | 학술논문

粒子滤波作为一种基于贝叶斯估计的算法,在处理非线性运动目标跟踪问题上具有特殊的优势.基于此,提出一种基于粒子滤波和Mean-shift的混合跟踪算法(KMSEPF).KMSEPF算法对一般的Mean-shift和料子滤波混合算法进行改进.结果证明,KMSEPF算法与混合算法MSEPF相比,在计算效率提高的同时,跟踪准确性和处理遮挡的能力没有下降.
입자려파작위일충기우패협사고계적산법,재처리비선성운동목표근종문제상구유특수적우세.기우차,제출일충기우입자려파화Mean-shift적혼합근종산법(KMSEPF).KMSEPF산법대일반적Mean-shift화료자려파혼합산법진행개진.결과증명,KMSEPF산법여혼합산법MSEPF상비,재계산효솔제고적동시,근종준학성화처리차당적능력몰유하강.
As an algorithm based on Bayesian estimation,particle filtering is predominant on tracking nonlinear moving target.This paper proposes an algorithm,which is based on Mean-shift and particle filtering,named K-means and Mean-shift Embedded Particle Filter(KMSEPF).The KMSEPF algorithm improves the general mixture algorithms which are based on particle filtering and Mean-shift.Results show that the algorithm reduces the computation complexity,while maintains the high precision and the ability to control the occlusion,compared with the MSEPF algorithm.