Abstract

A detection algorithm, based on the combined local-global (CLG) optical-flow model and Gaussian pyramid for a moving target appearing against a dynamic background, can compensate for the inadaptability of the classic Horn–Schunck algorithm to illumination changes and reduce the number of needed calculations. Incorporating the hypothesis of gradient conservation into the traditional CLG optical-flow model and combining structure and texture decomposition enable this algorithm to minimize the impact of illumination changes on optical-flow estimates. Further, calculating optical-flow with the Gaussian pyramid by layers and computing optical-flow at other points using an optical-flow iterative with higher gray-level points together reduce the number of calculations required to improve detection efficiency. Finally, this proposed method achieves the detection of a moving target against a dynamic background, according to the background motion vector determined by the displacement and magnitude of the optical-flow. Simulation results indicate that this algorithm, in comparison to the traditional Horn-Schunck optical-flow algorithm, accurately detects a moving target undergoing illumination changes against a dynamic background and simultaneously demonstrates a significant reduction in the number of computations needed to improve detection efficiency.

© 2016 Optical Society of Korea

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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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    [Crossref]
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2014 (2)

W. Hu, T. Tan, and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews), 34(3), 334-352 (2014).

M. H. Jeong, “Image Blurring Estimation and Calibration with a Joint Transform Correlator,” J. Opt. Soc. Korea 18(5), 472-476 (2014).
[Crossref]

2013 (2)

2010 (2)

W. H. Lee, “Foreground objects detection using multiple difference images,” Opt. Eng. 49(4), 047201 (2010).

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

2006 (1)

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657-662 (2006).
[Crossref]

1998 (1)

T. Meier and K. Ngan, “Automatic segmentation of moving objects for video object plane generation,” IEEE Trans. Circuits Syst. Video Technol. 8(5), 525-538 (1998).
[Crossref]

Andres, B.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

Arbelaez, P.

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Baker, S.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Black, M.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Brox, T.

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Christian, F.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

Christoph, S.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

Ding, L. H.

Z. Jiang and L. H. Ding, “Aerial video image object detection and tracing based on motion vector compensation and statistic analysis,” In Proc 2009 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (Shanghai, China, Nov. 2009), pp. 302-305.

Do, B. H.

B. H. Do and S. C. Huang, “Dynamic background modeling based on radial basis function neural networks for moving object detection,” In Proc 2011 IEEE International Conference on Multimedia and Expo, (Barcelona, Spain, Jul. 2011), pp. 1-4.

Feng, Y.

Fu, M.

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

Gong, J.

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

Haynor, D.

S. Sun, D. Haynor, and Y. Kim, “Motion estimation based on optical flow with adaptive gradients,” In Proc. 2000 International Conference on Image Processing, (Vancouver, Canada, Sept. 2000), pp. 852-855.

Heikkila, M.

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657-662 (2006).
[Crossref]

Hu, W.

W. Hu, T. Tan, and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews), 34(3), 334-352 (2014).

Huang, S. C.

B. H. Do and S. C. Huang, “Dynamic background modeling based on radial basis function neural networks for moving object detection,” In Proc 2011 IEEE International Conference on Multimedia and Expo, (Barcelona, Spain, Jul. 2011), pp. 1-4.

Jeong, M. H.

Jiang, Z.

Z. Jiang and L. H. Ding, “Aerial video image object detection and tracing based on motion vector compensation and statistic analysis,” In Proc 2009 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (Shanghai, China, Nov. 2009), pp. 302-305.

Joachim, W.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

Kim, Y.

S. Sun, D. Haynor, and Y. Kim, “Motion estimation based on optical flow with adaptive gradients,” In Proc. 2000 International Conference on Image Processing, (Vancouver, Canada, Sept. 2000), pp. 852-855.

Lee, W. H.

W. H. Lee, “Foreground objects detection using multiple difference images,” Opt. Eng. 49(4), 047201 (2010).

Lewis, J.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Liu, T.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

Maire, M.

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Malik, J.

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Maybank, S.

W. Hu, T. Tan, and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews), 34(3), 334-352 (2014).

Meier, T.

T. Meier and K. Ngan, “Automatic segmentation of moving objects for video object plane generation,” IEEE Trans. Circuits Syst. Video Technol. 8(5), 525-538 (1998).
[Crossref]

Ngan, K.

T. Meier and K. Ngan, “Automatic segmentation of moving objects for video object plane generation,” IEEE Trans. Circuits Syst. Video Technol. 8(5), 525-538 (1998).
[Crossref]

Pietikainen, M.

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657-662 (2006).
[Crossref]

Roth, S.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Scharstein, D.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Shum, H. Y.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

Sun, J.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic Saliency Using Background Priors,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 29-42.

Sun, S.

S. Sun, D. Haynor, and Y. Kim, “Motion estimation based on optical flow with adaptive gradients,” In Proc. 2000 International Conference on Image Processing, (Vancouver, Canada, Sept. 2000), pp. 852-855.

Sundberg, P.

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Sung, K. K.

Szeliski, R.

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

Tan, T.

W. Hu, T. Tan, and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews), 34(3), 334-352 (2014).

Tang, X.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

Timo, K.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

Vladimir, S.

Wei, Y.

Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic Saliency Using Background Priors,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 29-42.

Wen, F.

Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic Saliency Using Background Priors,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 29-42.

Wu, H. W.

Xia, J. M.

Xiong, G.

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

Yang, M. H.

K. Zhang, L. Zhang, and M. H. Yang, “Real-Time Compressive Tracking,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 864-877.

Yang, X.

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

Yang, Y.

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

Zhang, K.

K. Zhang, L. Zhang, and M. H. Yang, “Real-Time Compressive Tracking,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 864-877.

Zhang, L.

K. Zhang, L. Zhang, and M. H. Yang, “Real-Time Compressive Tracking,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 864-877.

Zhang, L. S.

Zhang, R. H.

Zheng, N. N.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

Zhu, W.

Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic Saliency Using Background Priors,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 29-42.

IEEE Trans. Circuits Syst. Video Technol. (1)

T. Meier and K. Ngan, “Automatic segmentation of moving objects for video object plane generation,” IEEE Trans. Circuits Syst. Video Technol. 8(5), 525-538 (1998).
[Crossref]

IEEE Trans. Pattern Anal. Mach. Intell. (1)

M. Heikkila and M. Pietikainen, “A texture-based method for modeling the background and detecting moving objects,” IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657-662 (2006).
[Crossref]

IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews) (1)

W. Hu, T. Tan, and S. Maybank, “A Survey on Visual Surveillance of Object Motion and Behaviors,” IEEE Trans. Syst., Man, Cybern., Syst., Part C (Applications and Reviews), 34(3), 334-352 (2014).

Int. J. Comput. Vision (1)

S. Baker, D. Scharstein, J. Lewis, S. Roth, M. Black, and R. Szeliski, “A Database and Evaluation Methodology for Optical Flow,” Int. J. Comput. Vision 92(1), 1-31 (2010).

J. Opt. Soc. Korea (3)

Opt. Eng. (1)

W. H. Lee, “Foreground objects detection using multiple difference images,” Opt. Eng. 49(4), 047201 (2010).

Other (9)

Y. Yang, M. Fu, X. Yang, G. Xiong, and J. Gong, “Autonomous ground vehicle navigation method in complex environment,” 2010 IEEE Intelligent Vehicles Symposium, (San Diego, CA, USA, Jun. 2010), pp. 458-460.

T. Liu, J. Sun, N. N. Zheng, X. Tang, and H. Y. Shum, “Learning to Detect a Salient Object,” In Proc 2007 IEEE Conference on Computer Vision and Pattern Recognition, (Minneapolis, MN, USA, Jun. 2007), pp. 1-8.

S. Sun, D. Haynor, and Y. Kim, “Motion estimation based on optical flow with adaptive gradients,” In Proc. 2000 International Conference on Image Processing, (Vancouver, Canada, Sept. 2000), pp. 852-855.

B. Andres, W. Joachim, F. Christian, K. Timo, and S. Christoph, “Real-Time Optic Flow Computation with Variational Methods,” Computer Analysis of Images and Patterns, (Springer Berlin Heidelberg, Germany, 2003), pp. 222-229

P. Sundberg, T. Brox, M. Maire, P. Arbelaez, and J. Malik, “Occlusion boundary detection and figure/ground assignment from optical flow,” IEEE Conference on Computer Vision and Pattern Recognition 2011, (Crowne Plaza, Colorado, USA, Jun. 2011), pp. 2233-2240.

Z. Jiang and L. H. Ding, “Aerial video image object detection and tracing based on motion vector compensation and statistic analysis,” In Proc 2009 IEEE Asia Pacific Conference on Postgraduate Research in Microelectronics and Electronics (Shanghai, China, Nov. 2009), pp. 302-305.

B. H. Do and S. C. Huang, “Dynamic background modeling based on radial basis function neural networks for moving object detection,” In Proc 2011 IEEE International Conference on Multimedia and Expo, (Barcelona, Spain, Jul. 2011), pp. 1-4.

Y. Wei, F. Wen, W. Zhu, and J. Sun, “Geodesic Saliency Using Background Priors,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 29-42.

K. Zhang, L. Zhang, and M. H. Yang, “Real-Time Compressive Tracking,” in Proc. 12th European conference on Computer Vision (Florence, Italy, Oct. 2012), pp. 864-877.

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