Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine

WANG Zhouchun; CUI Wennan; ZHANG Tao

Volume 43 Issue 2

Mar. 2021

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Article Navigation > Infrared Technology > 2021 > 43(2): 153-161

WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.

Citation:

WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.

WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.

Citation:

WANG Zhouchun, CUI Wennan, ZHANG Tao. Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine[J]. Infrared Technology , 2021, 43(2): 153-161.

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Classification and Recognition Algorithm for Long-wave Infrared Targets Based on Support Vector Machine

WANG Zhouchun^{1, 2, 3, 4
,},
CUI Wennan^{1, 2, 4},
ZHANG Tao^{1, 2, 3, 4
,
,}

1.
Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
2.
University of Chinese Academy of Sciences, Beijing 100049, China
3.
Shanghai Tech University, Shanghai 201210, China
4.
Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai 200083, China

Received Date: 2020-01-06
Rev Recd Date: 2020-01-31
Publish Date: 2021-02-20

Abstract

Abstract

Infrared images have a low resolution and a single color, but they play an important role in some scenes because they can be used under all weather conditions. This study adopts a support vector machine algorithm for long-wave infrared target image classification and recognition. The algorithm extracts edge and texture features, which are used as the recognition features of the target, and forwards them to a support vector machine. Then, the target category is output for infrared target recognition. Several models, such as the histogram of oriented gradient, gray level co-occurrence matrix, and support vector machine, are combined to collect images of eight types of target scenes for training and testing. The experimental results show that the algorithm can classify the same target person wearing different clothes with high accuracy and that it has a good classification effect on different target characters. Therefore, under certain scene conditions, this combined algorithm model can meet the needs and has certain advantages in the field of target recognition.
- long-wave infrared target,
- support vector machine,
- recognition feature,
- target recognition

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References(24)

References

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