A small target detection method in infrared image sequences based on compressive sensing and background subtraction

According to the feature that the small moving target is highly sparse relative to the sky background in infrared image, a novel adaptive background subtraction method was presented. The small target in infrared image sequence can be directly detected by using the method based on compressive sensing. It also can significantly reduce the sampling of signal and transmission bandwidth. The simulation results indicate that the proposed method is effective.

[1]  E.J. Candes.  Compressive Sampling , 2022 .

[2]  Nathan A. Goodman,et al.  Direct reconstruction of difference images from optimal spatial-domain projections , 2008, Optical Engineering + Applications.

[3]  Deanna Needell,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, ArXiv.

[4]  Aswin C. Sankaranarayanan,et al.  Compressive Sensing , 2008, Computer Vision, A Reference Guide.

[5]  R.G. Baraniuk,et al.  Compressive Sensing [Lecture Notes] , 2007, IEEE Signal Processing Magazine.

[6]  Massimo Fornasier,et al.  Compressive Sensing , 2015, Handbook of Mathematical Methods in Imaging.

[7]  Guo Xu-jing.  Detection of weak point moving targets in infrared image sequences , 2010 .

[8]  Emmanuel J. Candès,et al.  Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information , 2004, IEEE Transactions on Information Theory.

[9]  Massimo Piccardi,et al.  Background subtraction techniques: a review , 2004, 2004 IEEE International Conference on Systems, Man and Cybernetics (IEEE Cat. No.04CH37583).

[10]  Volkan Cevher,et al.  Compressive Sensing for Background Subtraction , 2008, ECCV.

[11]  S. Frick,et al.  Compressed Sensing , 2014, Computer Vision, A Reference Guide.

[12]  David L Donoho,et al.  Compressed sensing , 2006, IEEE Transactions on Information Theory.

[13]  J. Tropp,et al.  CoSaMP: Iterative signal recovery from incomplete and inaccurate samples , 2008, Commun. ACM.