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CLC number: TP391

On-line Access: 2016-11-07

Received: 2016-04-25

Revision Accepted: 2016-08-09

Crosschecked: 2016-10-09

Cited: 0

Clicked: 2077

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Jing-li Gao

http://orcid.org/0000-0002-4272-8767

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Frontiers of Information Technology & Electronic Engineering  2016 Vol.17 No.11 P.1176-1185

http://doi.org/10.1631/FITEE.1601203


Detecting slowly moving infrared targets using temporal filtering and association strategy


Author(s):  Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu

Affiliation(s):  College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China; more

Corresponding email(s):   gjl991@163.com, wencl@hdu.edu.cn, zjbao@zju.edu.cn, liumeiqin@zju.edu.cn

Key Words:  Temporal target detection, Slowly moving targets, Graph matching, Target association


Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu. Detecting slowly moving infrared targets using temporal filtering and association strategy[J]. Frontiers of Information Technology & Electronic Engineering, 2016, 17(11): 1176-1185.

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author="Jing-li Gao, Cheng-lin Wen, Zhe-jing Bao, Mei-qin Liu",
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pages="1176-1185",
year="2016",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601203"
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Abstract: 
The special characteristics of slowly moving infrared targets, such as containing only a few pixels, shapeless edge, low signal-to-clutter ratio, and low speed, make their detection rather difficult, especially when immersed in complex backgrounds. To cope with this problem, we propose an effective infrared target detection algorithm based on temporal target detection and association strategy. First, a temporal target detection model is developed to segment the interested targets. This model contains mainly three stages, i.e., temporal filtering, temporal target fusion, and cross-product filtering. Then a graph matching model is presented to associate the targets obtained at different times. The association relies on the motion characteristics and appearance of targets, and the association operation is performed many times to form continuous trajectories which can be used to help disambiguate targets from false alarms caused by random noise or clutter. Experimental results show that the proposed method can detect slowly moving infrared targets in complex backgrounds accurately and robustly, and has superior detection performance in comparison with several recent methods.

基于时域滤波和关联策略的红外慢速目标检测

概要:红外慢速目标的一些独特特性,例如像素少,不成形的边缘信息,低信杂比和低速等,使得它们的检测异常困难,而当被淹没在复杂背景中时尤甚。为了解决这一问题,根据时域目标检测和关联策略,本文提出了一种有效的红外目标检测算法。首先,建立一种时域目标检测模型对疑似目标进行分割,该模型主要包含三个阶段,时域滤波,时域目标融合和交叉积滤波;然后提出一种图模型,来关联不同时刻获取的疑似目标。这种关联依赖于目标的运动和表观特征,可进行多次关联操作获取目标轨迹,并据此区分真实目标和由噪声或杂波引起的虚假目标。实验结果表明本文提出的方法能够准确、鲁棒地检测复杂背景下的红外慢速目标,且与几种基准方法相比具有优越的检测性能。

关键词:时域目标检测;慢速移动目标;图匹配;目标关联

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