[1]张 笙,李郁峰,严云洋,等.面向鲁棒视觉监控的热红外与可见光视频融合运动目标检测[J].红外技术,2013,35(12):773-779.[doi:10.11846/j.issn.1001_8891.201312006]
 ZHANG Sheng,LI Yu-feng,YAN Yun-yang,et al.Moving Target Detection Using Fusion of Visual and Thermal Video for Robust Surveillance [J].Infrared Technology,2013,35(12):773-779.[doi:10.11846/j.issn.1001_8891.201312006]
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面向鲁棒视觉监控的热红外与可见光视频融合运动目标检测
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《红外技术》[ISSN:1001-8891/CN:CN 53-1053/TN]

卷:
35卷
期数:
2013年12期
页码:
773-779
栏目:
出版日期:
2013-12-20

文章信息/Info

Title:
Moving Target Detection Using Fusion of Visual and Thermal Video for
Robust Surveillance
文章编号:
1001-8891(2013)12-0773-07
作者:
张 笙1李郁峰1严云洋2徐铭蔚1熊 平3唐遵烈3
1.西南科技大学计算机科学与技术学院,四川 绵阳 621010;2.淮阴工学院计算机工程学院,江苏 淮安 223003;
3.中国电子科技集团公司第四十四研究所,重庆 400060
Author(s):
ZHANG Sheng1LI Yu-feng1YAN Yun-yang2XU Ming-wei1XIONG Ping3TANG Zun-lie3
1.School of Computer Science and Technology, Southwest University of Science and Technology, Mianyang 621010, China;
2.School of Computer Engineering, Huaiyin Institute of Technology, Huai’an 223003, China;
3.The 44th Research Institute, China Electronics Technology Group Corporation, Chongqing 400060, China
关键词:
运动目标检测视频监控热红外视频可见光视频数据融合
Keywords:
moving target detectionvideo surveillancethermal videovisible videodata fusion
分类号:
TP751.1
DOI:
10.11846/j.issn.1001_8891.201312006
文献标志码:
A
摘要:
在非受控环境中,由于背景的动态变化或光照、阴影的影响,执行高效、实时的运动目标检测具有很大的挑战性,联合长波红外(LWIR 8~14 mm)和可见光相机构成一个多模视觉系统可以显著提高运动目标检测的鲁棒性和完整性。提出了一种先检测后融合的运动目标检测算法,首先对可见光视频采用混合高斯建模方法检测运动目标,对热红外视频设计了基于背景差分和时间差分相结合的加权算法提取运动区域,然后对可见光与热红外视频中运动目标进行特征级融合。实验结果表明:该方法利用热红外与可见光图像的直观互补特征,在满足实时性要求的同时,可实现运动目标的精确、完整、鲁棒性检测。
Abstract:
In uncontrolled environments, because of the effect of dynamic background, lighting changes and shadows, it is challenging to perform an efficient and real-time moving target detection algorithm. Constructing a multi-mode visual surveillance system with long wave infrared(LWIR 8-14 ?m) and visible cameras can significantly improve the robustness and completeness of moving objects extraction. This paper presents a detection-fusion moving target detection algorithm. It starts from a Gaussian mixture background modeling algorithm for moving objects extraction in visible video and a weighted method based on background subtraction and the time-stepping for moving target detection in thermal video. The moving targets, obtained from visible and thermal video, are then fused at the feature level. The experimental results demonstrate that this method which uses the intuitive and complementary information from thermal and visual imagery can meet the real-time requirements, and can also get more complete, accurate and robust detection.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2013-08-02;修订日期:2013-09-13.
作者简介:张笙(1988-),男,江苏徐州人,硕士研究生,主要研究方向为图像处理。E-mail:422547756@qq.com。
通讯作者:李郁峰(1972-),男,陕西咸阳人,副教授,博士,主要研究方向为图像处理。E-mail:liyufeng@swust.edu.cn。
基金项目:西南科技大学研究生创新基金资助;中国电科集团公司CCD研发中心基础技术研究项目;西南科技大学网络融合工程实验室开放基金。
更新日期/Last Update: 2013-12-26