Complexity Estimation of Infrared Image Sequence for Automatic Target Track
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 infrare...
| Format: | Article |
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| Language: | zho |
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EDP Sciences
2019-08-01
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| Series: | Xibei Gongye Daxue Xuebao |
| Subjects: | |
| Online Access: | https://www.jnwpu.org/articles/jnwpu/full_html/2019/04/jnwpu2019374p664/jnwpu2019374p664.html |
| _version_ | 1797642670537965568 |
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| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T14:03:39Z |
| format | Article |
| id | doaj.art-c55976a4db9e43a9aa84674030b92a6e |
| institution | Directory Open Access Journal |
| issn | 1000-2758 2609-7125 |
| language | zho |
| last_indexed | 2024-03-11T14:03:39Z |
| publishDate | 2019-08-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Xibei Gongye Daxue Xuebao |
| spelling | doaj.art-c55976a4db9e43a9aa84674030b92a6e2023-11-02T03:24:05ZzhoEDP SciencesXibei Gongye Daxue Xuebao1000-27582609-71252019-08-0137466467210.1051/jnwpu/20193740664jnwpu2019374p664Complexity Estimation of Infrared Image Sequence for Automatic Target Track01234School of Astronautics, Northwestern Polytechnical UniversityShanghai Academy of Spaceflight TechnologySchool of Astronautics, Northwestern Polytechnical UniversitySchool of Astronautics, Northwestern Polytechnical UniversitySchool of Astronautics, Northwestern Polytechnical UniversityInfrared 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.https://www.jnwpu.org/articles/jnwpu/full_html/2019/04/jnwpu2019374p664/jnwpu2019374p664.htmlcomplexity of infrared image sequencesconfusion degree of targetoccultation degree of targetgrey relational method |
| spellingShingle | Complexity Estimation of Infrared Image Sequence for Automatic Target Track Xibei Gongye Daxue Xuebao complexity of infrared image sequences confusion degree of target occultation degree of target grey relational method |
| title | Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
| title_full | Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
| title_fullStr | Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
| title_full_unstemmed | Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
| title_short | Complexity Estimation of Infrared Image Sequence for Automatic Target Track |
| title_sort | complexity estimation of infrared image sequence for automatic target track |
| topic | complexity of infrared image sequences confusion degree of target occultation degree of target grey relational method |
| url | https://www.jnwpu.org/articles/jnwpu/full_html/2019/04/jnwpu2019374p664/jnwpu2019374p664.html |