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Dim moving target detection algorithm based on spatio-temporal classification sparse representation

•Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•T...

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Published in:Infrared physics & technology 2014-11, Vol.67, p.273-282
Main Authors: Li, Zhengzhou, Dai, Zhen, Fu, Hongxia, Hou, Qian, Wang, Zhen, Yang, Lijiao, Jin, Gang, Liu, Changju, Li, Ruzhang
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cited_by cdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43
cites cdi_FETCH-LOGICAL-c345t-8f98e9e5a6899e51981d84a3b776fafa5b06776383a6f15068bc258f183fbdc43
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container_issue
container_start_page 273
container_title Infrared physics & technology
container_volume 67
creator Li, Zhengzhou
Dai, Zhen
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Hou, Qian
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Jin, Gang
Liu, Changju
Li, Ruzhang
description •Spatio-temporal dictionary can characterize motion and morphology.•Target can be sparsely decomposed on target spatio-temporal dictionary.•Background can be sparsely decomposed on background spatio-temporal dictionary.•Target can be decomposed more sparsely on Gaussian spatio-temporal dictionary.•The residuals reconstructed by target and background atoms differ very visibly. A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. The target and background clutter can be sparsely decomposed over their corresponding spatio-temporal redundant dictionary, yet could not be sparsely decomposed on their opposite spatio-temporal redundant dictionary, and so their residuals after reconstruction by the prescribed number of target and background spatio-temporal atoms would differ very visibly. Some experimental results show this proposed approach could not only improve the sparsity more efficiently, but also enhance the target detection performance more effectively.
doi_str_mv 10.1016/j.infrared.2014.07.030
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A dim moving target detection algorithm based on spatio-temporal classification sparse representation, which can characterize the motion information and morphological feature of target and background clutter, is proposed to enhance the performance of target detection. A spatio-temporal redundant dictionary is trained according to the content of infrared image sequence, and then is subdivided into target spatio-temporal redundant dictionary describing moving target, and background spatio-temporal redundant dictionary embedding background by the criterion that the target spatio-temporal atom could be decomposed more sparsely over Gaussian spatio-temporal redundant dictionary. 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subjects Algorithms
Background spatio-temporal redundant dictionary
Classification
Clutter
Decomposition
Dictionaries
Dim target detection
Moving targets
Redundant
Representations
Signal sparse reconstruction
Spatio-temporal classification redundant dictionary
Target spatio-temporal redundant dictionary
title Dim moving target detection algorithm based on spatio-temporal classification sparse representation
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