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Dim moving target detection algorithm based on spatio-temporal classification sparse representation
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DOI：10.1016/j.infrared.2014.07.030.png)
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Abstract
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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. 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. (C) 2014 Elsevier B.V. All rights reserved.
Keywords:
Dim target detection
Spatio-temporal classification redundant dictionary
Target spatio-temporal redundant dictionary
Background spatio-temporal redundant dictionary
Signal sparse reconstruction
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