Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation

doi:10.3969/j.issn.1000-1093.2015.07.016

Abstract

Abstract: A dim moving target detection algorithm based on over-complete spatial-temporal dictionary and sparse representation is proposed. A spatial-temporal adaptive morphological over-complete dictionary is trained and constructed according to infrared image sequence. It can represent the motion information and morphological feature of target and background clutter. The spatial-temporal morphological over-complete dictionary is subdivided into two categories: target over-complete spatial-temporal dictionary for describing moving target, and background over-complete spatial-temporal dictionary for embedding background. The criteria adopted to distinguish the target spatial-temporal redundant dictionary from the background spatial-temporal redundant dictionary is that the atom in target over-complete spatial-temporal dictionary could be decomposed more sparsely over Gaussian over-complete spatial-temporal dictionary. Subsequently, the image sequence is decomposed on the target and background over-complete spatial-temporal dictionaries, respectively. The dim moving target and background clutter can be sparsely decomposed on their corresponding over-complete spatial-temporal dictionary, yet it couldn’t be sparsely decomposed on their background over-complete spatial-temporal dictionary. Therefore, the target and background clutter would be reconstructed effectively by prescribed number of atoms with maximum sparse coefficients in their corresponding over-complete spatial-temporal dictionary, and their residuals would differ so visibly to distinguish target from background clutter. The results show that the proposed approach not only could improve the sparsity more efficiently for dim target image sequence, but also could improve the performance of small target detection.

Key words: information processing technology, dim target detection, spatial-temporal redundant dictionary, target spatial-temporal redundant dictionary, background spatial-temporal redundant dictionary, signal sparse reconstruction

LI Zheng-zhou, HOU Qian, DAI Zhen, FU Hong-xia, GE Feng-zeng, JIN Gang. Dim Moving Target Detection Algorithm Based on Spatial-temporal Sparse Representation[J]. Acta Armamentarii, 2015, 36(7): 1273-1279.

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