Static compressive tracking

doi:10.1364/OE.20.021160

Static compressive tracking

D. J. Townsend, P. K. Poon, S. Wehrwein, T. Osman, A. V. Mariano, E. M. Vera, M. D. Stenner, and M. E. Gehm »View Author Affiliations

Optics Express, Vol. 20, Issue 19, pp. 21160-21172 (2012)
http://dx.doi.org/10.1364/OE.20.021160

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References (14)
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Abstract

This paper presents the Static Computational Optical Undersampled Tracker (SCOUT), an architecture for compressive motion tracking systems. The architecture uses compressive sensing techniques to track moving targets at significantly higher resolution than the detector array, allowing for low cost, low weight design and a significant reduction in data storage and bandwidth requirements. Using two amplitude masks and a standard focal plane array, the system captures many projections simultaneously, avoiding the need for time-sequential measurements of a single scene. Scenes with few moving targets on static backgrounds have frame differences that can be reconstructed using sparse signal reconstruction techniques in order to track moving targets. Simulations demonstrate theoretical performance and help to inform the choice of design parameters. We use the coherence parameter of the system matrix as an efficient predictor of reconstruction error to avoid performing computationally intensive reconstructions over the entire design space. An experimental SCOUT system demonstrates excellent reconstruction performance with 16X compression tracking movers on scenes with zero and nonzero backgrounds.

OCIS Codes
(100.3190) Image processing : Inverse problems
(110.1758) Imaging systems : Computational imaging
(100.4999) Image processing : Pattern recognition, target tracking

ToC Category:
Image Processing

History
Original Manuscript: May 25, 2012
Revised Manuscript: August 26, 2012
Manuscript Accepted: August 27, 2012
Published: August 31, 2012

Citation
D. J. Townsend, P. K. Poon, S. Wehrwein, T. Osman, A. V. Mariano, E. M. Vera, M. D. Stenner, and M. E. Gehm, "Static compressive tracking," Opt. Express 20, 21160-21172 (2012)
http://www.opticsinfobase.org/oe/abstract.cfm?URI=oe-20-19-21160

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References

M. Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly, and R. Baraniuk, “An architecture for compressive imaging,” in Proceedings of IEEE Intl. Conference on Image Processing, (IEEE, 2006), pp. 1273–1276.
M. Lustig, D. Donoho, and J. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med.58, 1182–1195 (2007). [CrossRef] [PubMed]
R. Willett, R. Marcia, and J. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50, 072601 (2011). [CrossRef]
M. Neifeld and J. Ke, “Optical architectures for compressive imaging,” Appl. Opt.46, 5293, (2007). [CrossRef] [PubMed]
M. Stenner, D. Townsend, and M. Gehm, “Static architecture for compressive motion detection in persistent, pervasive surveillance applications,” in Imaging Systems, OSA Technical Digest Series (Optical Society of America, 2010), paper IMB2.
Y. Rivenson, A. Stern, and B. Javidi, “Single exposure super-resolution compressive imaging by double phase encoding,” Opt. Express18, 15094–15103 (2010). [CrossRef] [PubMed]
Y. Kashter, O. Levi, and A. Stern, “Optical compressive change and motion detection,” Appl. Opt.51, 2491–2496 (2012). [CrossRef] [PubMed]
W. Bajwa, J. Haupt, G. Raz, S. Wright, and R. Nowak, “Toeplitz-structured compressed sensing matrices,” in Proceedings of IEEE Workshop on Statistical Signal Processing, (IEEE, 2007), pp. 294–298. [CrossRef]
H. Rauhut, “Circulant and Toeplitz matrices in compressed sensing,” http://arxiv.org/abs/0902.4394 .
J. Romberg, “Compressive sensing by random convolution,” SIAM J. Imaging Sci.2, 1098–1128 (2009). [CrossRef]
F. Sebert, Y. Zou, and L. Ying, “Toeplitz block matrices in compressed sensing and their applications in imaging,” in Proceedings of IEEE International Conference on Information Technology and Applications in Biomedicine, (IEEE, 2008), pp. 47–50. [CrossRef]
B. Liu, F. Sebert, Y. Zou, and L. Ying, “SparseSENSE: randomly-sampled parallel imaging using compressed sensing,” in Proceedings of the 16th Annual Meeting of ISMRM3154 (2008).
S. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An interior-point method for large-scale l1-regularized least squares,” IEEE J. Sel. Top. Sig. Proc.1, 606–617 (2007). [CrossRef]
J. Tropp, “Just relax: Convex programming methods for identifying sparse signals in noise,” IEEE Trans. Inf. Theory52, 1030–1051 (2006). [CrossRef]

Bajwa, W.
Baraniuk, R.
Baron, D.
Boyd, S.
Donoho, D.
Duarte, M.
Gehm, M.
Gorinevsky, D.
Haupt, J.
Javidi, B.
Kashter, Y.
Ke, J.
Kelly, K.
Kim, S.
Koh, K.
Laska, J.
Levi, O.
Liu, B.
Lustig, M.
Marcia, R.
Neifeld, M.
Nichols, J.
Nowak, R.
Pauly, J.
Raz, G.
Rivenson, Y.
Romberg, J.
Sarvotham, S.
Sebert, F.
Stenner, M.
Stern, A.
Takhar, D.
Townsend, D.
Tropp, J.
Wakin, M.
Willett, R.
Wright, S.
Ying, L.
Zou, Y.

Appl. Opt.

IEEE J. Sel. Top. Sig. Proc.

S. Kim, K. Koh, M. Lustig, S. Boyd, and D. Gorinevsky, “An interior-point method for large-scale l1-regularized least squares,” IEEE J. Sel. Top. Sig. Proc.1, 606–617 (2007). [CrossRef]

IEEE Trans. Inf. Theory

J. Tropp, “Just relax: Convex programming methods for identifying sparse signals in noise,” IEEE Trans. Inf. Theory52, 1030–1051 (2006). [CrossRef]

Magn. Reson. Med.

M. Lustig, D. Donoho, and J. Pauly, “Sparse MRI: The application of compressed sensing for rapid MR imaging,” Magn. Reson. Med.58, 1182–1195 (2007). [CrossRef] [PubMed]

Opt. Eng.

R. Willett, R. Marcia, and J. Nichols, “Compressed sensing for practical optical imaging systems: a tutorial,” Opt. Eng.50, 072601 (2011). [CrossRef]

Opt. Express

Y. Rivenson, A. Stern, and B. Javidi, “Single exposure super-resolution compressive imaging by double phase encoding,” Opt. Express18, 15094–15103 (2010). [CrossRef] [PubMed]

Proceedings of the 16th Annual Meeting of ISMRM

B. Liu, F. Sebert, Y. Zou, and L. Ying, “SparseSENSE: randomly-sampled parallel imaging using compressed sensing,” in Proceedings of the 16th Annual Meeting of ISMRM3154 (2008).

SIAM J. Imaging Sci.

J. Romberg, “Compressive sensing by random convolution,” SIAM J. Imaging Sci.2, 1098–1128 (2009). [CrossRef]

Other

F. Sebert, Y. Zou, and L. Ying, “Toeplitz block matrices in compressed sensing and their applications in imaging,” in Proceedings of IEEE International Conference on Information Technology and Applications in Biomedicine, (IEEE, 2008), pp. 47–50. [CrossRef]
M. Wakin, J. Laska, M. Duarte, D. Baron, S. Sarvotham, D. Takhar, K. Kelly, and R. Baraniuk, “An architecture for compressive imaging,” in Proceedings of IEEE Intl. Conference on Image Processing, (IEEE, 2006), pp. 1273–1276.
W. Bajwa, J. Haupt, G. Raz, S. Wright, and R. Nowak, “Toeplitz-structured compressed sensing matrices,” in Proceedings of IEEE Workshop on Statistical Signal Processing, (IEEE, 2007), pp. 294–298. [CrossRef]
H. Rauhut, “Circulant and Toeplitz matrices in compressed sensing,” http://arxiv.org/abs/0902.4394 .
M. Stenner, D. Townsend, and M. Gehm, “Static architecture for compressive motion detection in persistent, pervasive surveillance applications,” in Imaging Systems, OSA Technical Digest Series (Optical Society of America, 2010), paper IMB2.

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