Infrared target tracking based on selective convolution features

Qian Kun; Yang Junyan; Yu Yue; Zhao Dong; Rong Shenghui

doi:10.11884/HPLPB201931.190133

Volume 31 Issue 9

Sep. 2019

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Article Navigation > High Power Laser and Particle Beams > 2019 > 31(9): 093202

Qian Kun, Yang Junyan, Yu Yue, et al. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31: 093202. doi: 10.11884/HPLPB201931.190133

Citation:

Qian Kun, Yang Junyan, Yu Yue, et al. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31: 093202. doi: 10.11884/HPLPB201931.190133

Qian Kun, Yang Junyan, Yu Yue, et al. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31: 093202. doi: 10.11884/HPLPB201931.190133

Citation:

Qian Kun, Yang Junyan, Yu Yue, et al. Infrared target tracking based on selective convolution features[J]. High Power Laser and Particle Beams, 2019, 31: 093202. doi: 10.11884/HPLPB201931.190133

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Infrared target tracking based on selective convolution features

doi: 10.11884/HPLPB201931.190133

Qian Kun^{1, 2
,},
Yang Junyan^{1, 2},
Yu Yue^{1, 2},
Zhao Dong³,
Rong Shenghui⁴

1.
Shanghai Institute of Spaceflight Control Technology, Shanghai 201109, China
2.
Infrared Detection Technology Research and Development Center of China Aerospace Science and Technology Corporation, Shanghai 201109, China
3.
School of Physics and Optoelectronic Engineering, Xidian University, Xi'an 710071, China
4.
School of Information Science and Engineering, Ocean University of China, Qingdao 266100, China

Received Date: 2019-04-17
Rev Recd Date: 2019-06-06
Publish Date: 2019-09-15

Abstract

Abstract

Infrared target tracking is heavily influenced by illumination variation, small size and complex background, and the lack of target information makes the algorithm lose targets easily. Therefore, an algorithm based on convolution features and feature selection method is presented in this paper to track IR targets. First, several filters in target patches of the first frame are used to obtain strong features. Then, the boosting method is utilized to train the features with redundant information, thus, the algorithm performance of accuracy and execution efficiency can be improved. Finally, particle weights are represented by the response of the native Bayes classifier. Experimental results show that the presented algorithm obtains good performance.
- infrared image,
- target tracking,
- dim-small target,
- convolutional feature,
- boosting,
- particle filter

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References(16)

References

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