Tracking of feature points in image sequence by SMC implementation of PHD filter

被引：0

作者：

Ikoma, N ^{[1
]}

Uchino, T ^{[1
]}

Maeda, H ^{[1
]}

机构：

[1] Kyushu Inst Technol, Fac Engn, Fukuoka 8048550, Japan

来源：

SICE 2004 ANNUAL CONFERENCE, VOLS 1-3 | 2004年

关键词：

image sequence; tracking; Sequential Monte Carlo; PHD filter;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

We investigate a method for filtering of feature points' trajectories in image sequence by using a novel technique named Sequential Monte Carlo (SMC) implementation of Probability Hypothesis Density (PHD) filter. PHD filter uses finite random set (FRS) on state space to represent and to track multiple targets in clutter. It can deal with appearance/disappearance of target due to the FRS representation. PHD is 1st order moment of finite random set, which corresponds to mean vector of the Kalman filter in continuous variable state case. SMC implementation of PHD filter is an elaborated filter that approximates the PHD by many number of realization, which are called particles, and it properly control the number of particles according to appearance/disappearance of targets. We apply this idea to track trajectories of feature points in image sequence. Simulation and real image analysis show the efficiency of the method.

引用

页码：1696 / 1701

页数：6

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