[1]陈海欣,顾国华,钱惟贤,等. 基于TMS320C6678的背景更新算法的改进与实现[J].红外技术,2013,35(01):021-26.
 CHEN Hai-xin,GU Guo-hua,QIAN Wei-xian,et al. Improvement and Implementation of Background Updating Algorithm Based on TMS320C6678[J].Infrared Technology,2013,35(01):021-26.
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 基于TMS320C6678的背景更新算法的改进与实现
分享到:

《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
35卷
期数:
2013年01期
页码:
021-26
栏目:
出版日期:
2013-01-31

文章信息/Info

Title:
 Improvement and Implementation of Background Updating Algorithm Based on TMS320C6678
文章编号:
1001-8891(2013)01-0021-06
作者:
 陈海欣顾国华钱惟贤陈钱徐富元
南京理工大学电光学院
Author(s):
 CHEN Hai-xinGU Guo-huaQIAN Wei-xianCHEN QianXU Fu-yuan
 College of Electric & Optic, Nanjing University of Science and Technology
关键词:
 序列图像自适应混合高斯模型背景更新框架TMS320C6678
Keywords:
image sequencesadaptive Gaussian mixture modelbackground updating frameworkTMS320C6678
分类号:
TN919.81
文献标志码:
A
摘要:
 

利用混合高斯模型描述序列图像背景是地面复杂背景下红外搜索跟踪系统中的关键技术。针对背景更新算法的收敛性和估计

准确性的问题,提出了一种自适应混合高斯模型的背景更新框架。该背景更新框架通过建立混合模型迭代过程中学习因子与时间、

模型状态的关系并且自适应选择混合模型中高斯分布的个数,有效地提高了背景估计的精确程度和算法的收敛速度。通过真实序列

图像详细分析本文背景更新框架性能,同时研究设计了基于TMS320C6678DSP芯片的背景更新图像处理系统,实现了视频的传输、处

理等系列功能,并与其他算法作比较,从数据分析和实验结果等方面表明本文算法的优越性。

Abstract:
 

It is a key technology in infrared target searching via Gaussian mixture model to describe the background

image sequences. In this paper, an adaptive Gaussian mixture model for background updating framework is proposed to

solve the problem of the convergence and accuracy. Based on the framework, the adaptive Gaussian mixture model

number and the relationship among learning factor, time and model state are built to improve the efficiency and

robustness. This paper realized the design of the adaptive background updating model based on C6678DSP chip with

video transmission, processing and other family functions. Experimental evaluation results for the real image

sequences validate the upgraded estimation capability of the proposed method compared with other algorithms.

参考文献/References:

[1] Stauffer C, Grimson W. Adaptive background mixture models for real-time tracking[C]//Proc. IEEE Conference on

Computer Vision and Pattern Recognition, 1999, 1: 246-252.
[2] 何亮明, 覃荣华, 巩思亮, 王营冠. 动态场景中的改进混合高斯背景模型[J]. 计算机工程, 2012, 8(38): 10-15.
[3] KaewTraKulPong P, Bowden R. An improved adaptive background mixture model for real-time tracking with shadow

detection[C]//Proc. European Workshop Advanced Video Based Surveillance Systems, 2001.
[4] Lee D S. Effective Gaussian mixture learning for video background subtraction[J]. IEEE Trans. Pattern Analysis

And Machine Intelligence, 2005, 27(5): 827-832.
[5] Zivkovic Z. Improved adaptive Gaussian mixture model for background subtraction[C]//Proc. of The 17th

International Conference on Pattern Recognition, 2004.
[6] Zhao Qinpei, Hautamäki Ville, Kärkkäinen Ismo, et al. Random swap EM algorithm for Gaussian mixture models[J].

Pattern Recognition Letters, 2012, 16(33): 2120-2126.
[7] Z Zivkovic, F Van der Heijden. Recursive unsupervised learning of finite mixture models[J]. IEEE Trans. on

PAMI, 2004, 26(5): 651-656.
[8] Z Zivkovic, F van der Heijden. Efficient adaptive density estimation per image pixel for the task of

background subtraction[J]. Pattern Recogn- ition Letters, 2006, 27(7): 773-780.

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备注/Memo

备注/Memo:
收稿日期:2012-10-25;修订日期:2012-12-26.
作者简介:陈海欣(1989-),男,硕士研究生,研究方向为红外图像处理,红外目标搜索跟踪。
基金项目:国家自然科学基金(61271332);江苏省“六大人才高峰”支持计划(2010-DZXX-022);总装预先研究基金。
更新日期/Last Update: 2013-02-04