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论文:2019,Vol:37,Issue(4):664-672 |
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引用本文： |
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王晓田, 马万超, 张凯, 李少毅, 闫杰. 面向自动目标跟踪红外图像序列复杂度度量[J]. 西北工业大学学报 |
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WANG Xiaotian, MA Wanchao, ZHANG Kai, LI Shaoyi, YAN Jie. Complexity Estimation of Infrared Image Sequence for Automatic Target Track[J]. Northwestern polytechnical university |
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| 面向自动目标跟踪红外图像序列复杂度度量 |
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| 王晓田1, 马万超2, 张凯1, 李少毅1, 闫杰1 |
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1. 西北工业大学 航天学院, 陕西 西安 710072;
2. 上海航天技术研究院, 上海 210009) |
| 摘要: |
| 红外图像复杂度度量是自动目标识别及其跟踪性能评估的重要组成部分。传统的度量指标如统计方差、信杂比等针对的皆是单帧图像，而对于图像序列复杂度度量的研究寥寥无几。针对该问题，提出一种面向自动目标跟踪的红外图像序列复杂度度量方法。首先，对影响目标识别及其跟踪因素进行分析，明确了红外图像序列中影响目标识别及其跟踪的具体原因，以此为依据构建基于特征空间的目标混淆度和目标遮隐度指标；其次，通过灰色关联法优化特征空间，使目标混淆度和目标遮隐度指标更加合理；最后，结合识别与跟踪的特点，选择合适的加权平均函数和非线性变换函数，实现图像序列复杂度度量。实验表明，与图像序列评价指标如序列相关度、帧间目标变化度相比，文中提出的评价指标与跟踪误差的单调关系更好，是一种有效的图像序列复杂度评价标准。 |
| 关键词:
序列复杂度
目标混淆度
目标遮隐度
灰色关联法
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| Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
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| WANG Xiaotian1, MA Wanchao2, ZHANG Kai1, LI Shaoyi1, YAN Jie1 |
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1. School of Astronautics, Northwestern Polytechnical University, Xi'an 710072, China;
2. Shanghai Academy of Spaceflight Technology, Shanghai 210009, China |
| Abstract: |
| Infrared image complexity metrics are an important task of automatic target recognition and track performance assessment. Traditional metrics, such as statistical variance and signal-to-noise ratio, targeted to single frame infrared image. However, there are some studies on the complexity of infrared image sequences. For this problem, a method to measure the complexity of infrared image sequence for automatic target recognition and track is proposed. Firstly, based on the analysis of the factors affecting the target recognition and track, the specific reasons which background influences target recognition and track are clarified, and the method introduces the feature space into confusion degree of target and occultation degree of target respectively. Secondly, the feature selection is carried out by using the grey relational method, and the feature space is optimized, so that confusion degree of target and occultation degree of target are more reasonable, and statistical formula F1-Score is used to establish the relationship between the complexity of single-frame image and the two indexes. Finally, the complexity of image sequence is not a linear sum of the single-frame image complexity. Target recognition errors often occur in high-complexity images and the target of low-complexity images can be correctly recognized. So the neural network Sigmoid function is used to intensify the high-complexity weights and weaken the low-complexity weights for constructing the complexity of image sequence. The experimental results show that the present metric is more valid than the other, such as sequence correlation and inter-frame change degree, has a strong correlation with the automatic target track algorithm, and which is an effective complexity evaluation metric for image sequence. |
| Key words:
complexity of infrared image sequences
confusion degree of target
occultation degree of target
grey relational method
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| 收稿日期: 2018-06-04
修回日期:
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| DOI: 10.1051/jnwpu/20193740664 |
| 基金项目: 国家自然科学基金（61703337）与上海航天科技创新基金（SAST2017-082）资助 |
| 通讯作者:
Email： |
| 作者简介: 王晓田(1989-),西北工业大学博士研究生,主要从事图像复杂度和红外目标检测、跟踪研究。
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| 作者相关文章 |
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| 王晓田 在本刊中的所有文章 |
| 马万超 在本刊中的所有文章 |
| 张凯 在本刊中的所有文章 |
| 李少毅 在本刊中的所有文章 |
| 闫杰 在本刊中的所有文章 |
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