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

• 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
• Format: Article
• Language: English
• 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
• ISSN: 1350-4495; 1879-0275
• DOI: 10.1016/j.infrared.2014.07.030

•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.