复杂天空背景下的红外弱小目标跟踪

赵东; 周慧鑫; 于君娜; 王士成; 秦翰林; 成宽洪

doi:10.11884/HPLPB201830.170511

复杂天空背景下的红外弱小目标跟踪

doi: 10.11884/HPLPB201830.170511

1.
西安电子科技大学物理与光电工程学院, 西安 710071
2.
中国电子科技集团公司第五十四研究所, 石家庄 050081

基金项目:

国家自然科学基金项目 61401343

国家自然科学基金项目 61675160

国家高技术研究发展计划项目 2014AA8098089C

中科院光谱成像技术重点实验室开放基金项目 LSIT201503

高等学校学科创新引智计划项目 B17035

详细信息

作者简介:
赵东(1989—)，男，博士，主要从事红外目标检测与跟踪方面的研究；dzhao@stu.xidian.edu.cn

通讯作者:
周慧鑫(1975—)，男，博士，教授，主要从事光电成像及图像处理、目标检测跟踪及制导等方面的研究；hxzhou@mail.xidian.edu.cn

中图分类号: TP391.4
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- 被引次数: 0
出版历程
- 收稿日期: 2017-12-19
- 修回日期: 2018-02-09
- 刊出日期: 2018-06-15

Tracking of infrared dim small target in complex sky background

1.
School of Technical Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071, China
2.
The 54th Research Institute of China Electronics Technology Group Corporation, Shijiazhuang 050081, China

摘要

摘要: 为解决传统跟踪算法不能有效区分复杂天空云层背景边缘和红外弱小目标，从而在跟踪过程中产生“偏移”的问题。在时空上下文原理基础上分析跟踪“偏移”的原因，引入高斯曲率滤波，提出一种改进的时空上下文红外弱小目标跟踪算法。该算法首先采用高斯曲率滤波对上下文区域进行预处理，在保留上下文区域背景边缘的同时剔除高频的红外弱小目标和噪声，从而获得准确的红外弱小目标置信图，利用红外弱小目标置信图估计出红外弱小目标位置。采用四组复杂天空背景下的红外弱小目标图像序列进行实验，并与经典的模板匹配算法、基于粒子滤波的均值漂移算法和快速压缩跟踪算法三种跟踪算法作比较。实验结果表明，算法在主观视觉和客观评价指标方面均优于其他三种算法，具有更高的目标跟踪精度与较好的实时性，可以实现对复杂天空背景下红外弱小目标的有效跟踪。
- 目标跟踪 /
- 红外弱小目标 /
- 高斯曲率滤波 /
- 时空上下文 /
- 傅里叶变换
Abstract: The complex sky cloud background edge and infrared dim small target cannot be distinguished using traditional tracking algorithms, and the "deviation" problem is produced when tracking the target. To solve this problem, this paper proposes a target tracking algorithm based on Gaussian curvature filter (GCF) and spatio-temporal context (STC) after introducing the STC theory and analyzing the reason of "deviation". First of all, the context areas are dealt with GCF which can reserve the image edge and eliminate the noise and the infrared dim small target, so as to obtain the infrared dim small target's accurate confidence map. Then the infrared dim small target's coordinates are confirmed by the confidence map. Four groups of the infrared dim small target sequences in the complex sky background experimental results prove that the proposed algorithm is effective, compared with several classical algorithms, such as TMT, MS-PF and FCT. The proposed algorithm has better performance than the three traditional algorithms on subjective vision and objective evaluating indicator and shows higher tracking accuracy and better real time performance. The infrared dim small target in complex sky cloud background can be effectively tracked with this algorithm.
- target tracking /
- infrared dim small target /
- Gaussian curvature filter /
- spatio-temporal context /
- Fourier transform

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图 1 上下文先验图处理结果

Figure 1. Context prior Result of processing

下载: 全尺寸图片幻灯片

图 2 不同云层背景下跟踪结果截图

Figure 2. Screenshots of tracking results on different background

下载: 全尺寸图片幻灯片

图 3 中心误差

Figure 3. Center location error

下载: 全尺寸图片幻灯片

表 1 实验图像序列指标统计

Table 1. Statistics of image sequences used as candidates of experiments

	image size/pixel	sequence length/frame	object size/pixel	noise scale	speed/(pixel·frame^-1)	motion trail
moving cloud	320×320	300	3×3	large	0.5	curve
thick cloud	400×400	300	3×3	very large	1	straight line
plane 1	256×256	63	2×2~6×6	very large	0.4~0.8	—
plane 2	570×320	375	3×3~7×7	little	0.4~0.8	—

下载: 导出CSV

表 2 跟踪算法性能比较

Table 2. Performance comparision of tracking algorithms

	success rate(SR)				average SR	average FPS
	moving cloud	thick cloud	plane 1	plane 2	average SR	average FPS
TMT	0.11	0.05	0	0	0.04	21
MS-PF	0.53	0.01	1	0.59	0.53	25
FCT	0.84	0.27	0.01	0.48	0.40	41
GCF-STC	1	1	0.99	0.99	0.99	54

下载: 导出CSV

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