CAJ | 학술논문

针对可变数目多目标视频跟踪，粒子滤波不能持续维持目标的多模态分布问题，本文提出一种混合粒子概率假设密度( PHD)滤波的多目标视频跟踪算法。该算法首先用K-means算法对粒子进行空间分布聚类，给各粒子群附加身份标签，使各粒子群分别对应混合粒子滤波的各分量，采用相互独立的各分量粒子滤波跟踪各目标，这样提高了目标状态估计的准确性，也能有效维持各目标的多模态分布。实验结果表明，该算法能有效处理新目标出现、合并、分离等多目标跟踪问题。
침대가변수목다목표시빈근종，입자려파불능지속유지목표적다모태분포문제，본문제출일충혼합입자개솔가설밀도( PHD)려파적다목표시빈근종산법。해산법수선용K-means산법대입자진행공간분포취류，급각입자군부가신빈표첨，사각입자군분별대응혼합입자려파적각분량，채용상호독립적각분량입자려파근종각목표，저양제고료목표상태고계적준학성，야능유효유지각목표적다모태분포。실험결과표명，해산법능유효처리신목표출현、합병、분리등다목표근종문제。
Aiming at the problem that particle filter is poor at consistently maintaining the multi-modality of the target distributions for multi-targets in a variable number of visual tracking, a multi-target visual tracking approach based on mixture particle probability hypothesis density ( PHD) filter is proposed. The particles are clustered by the K-means algorithm, the classified particles are labeled and the particle filters are separately used for each classified particles. It improves the accuracy of target states estimation and effectively maintains the multi-modal distribution of the various objectives. The experimental results show that the proposed approach is an effective solution to the appearance, merger, separation and other multi-target tracking problems for the new target.