Tracking Occlusive Target Method Based on Bayesian Decision Theory and Particle Filter

ZHOU Yan; HU Bo; ZHANG Jian-qiu

您当前的位置：

首页 >

文章列表页 >

Tracking Occlusive Target Method Based on Bayesian Decision Theory and Particle Filter

更新时间：2025-07-16

- Tracking Occlusive Target Method Based on Bayesian Decision Theory and Particle Filter
- Acta Electronica Sinica Vol. 35, Issue 2, Pages: 350-353(2007)
- 作者机构：
  
  复旦大学电子工程系,上海,200437
- 作者简介：
- 基金信息：
- DOI：
  CLC： TP391.4
- Published：2007
- 稿件说明：
移动端阅览
ZHOU Yan, HU Bo, ZHANG Jian-qiu. Tracking Occlusive Target Method Based on Bayesian Decision Theory and Particle Filter[J]. Acta Electronica Sinica, 2007, 35(2): 350-353.
DOI：

ZHOU Yan, HU Bo, ZHANG Jian-qiu. Tracking Occlusive Target Method Based on Bayesian Decision Theory and Particle Filter[J]. Acta Electronica Sinica, 2007, 35(2): 350-353. DOI：

摘要

为了跟踪遮挡目标

本文通过赋予外界遮挡和目标自身外观变化不同的风险度

利用风险决策的方法判断遮挡

设计了新的目标模板和模板更新规则

使目标模板既能随外观的缓慢变化自动更新

又能在全遮挡出现时维持模板不变.当这个新的目标模板和模板更新规则结合粒子滤波器的跟踪算法时

可实现遮挡目标的跟踪.实际视频流的测试结果表明

本文提出的方法可以有效判断遮挡

实现了长时间全遮挡情况下的目标跟踪.

Abstract

In order to track an occluded target in an image sequence

the Bayesian decision theory is

here

introduced to the problem of distinguishing occlusions and appearance changes according to their different risk possibilities.A new target template combining image intensity and histogram is designed.The corresponding updating method is also derived based on particle filter.If the target is totally occluded by another target

the template can be kept unchanged.The occlusion of a target will not influence tracking.Simulation results show that the presented method can efficiently justify whether the occlusion occurs and realize target tracking in image sequences even though the tracked target is totally occluded with long time.

关键词

Keywords

references

Views

1070

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Target Tracking Based on Adaptive Particle Filter Under Complex Background

A New Method of Maneuvering Target Tracking Based on Minimum Fuzzy Error Entropy

MFOPA Tracker with Dynamic Adaptive Feature Fusion

Application of Improved Cascade R-CNN Algorithm in Target Detection

Related Author

CHANG Fa-liang

MA Li

LIU Zeng-xiao

QIAO Yi-zheng

CHEN Yong-yin

LIU Quan-zhong

LI Liang-qun

KANG Li

Related Institution

School of Control Science and Engineering, Shandong University

College of Electronics and Information Engineering， Shenzhen University

Guangdong Key Laboratory of Intelligent Information Processing， Shenzhen University

China Greatwall Technology Group Co.， Ltd

School of Electronics and Control Engineering， Chang'an University

⁰