Abstract

Sea-surface targets are automatically detected and tracked using the bag-of-features (BOF) technique with the scale-invariant feature transform (SIFT) in infrared (IR) and visual (VIS) band videos. Features corresponding to the sea-surface targets and background are first clustered using a training set offline, and these features are then used for online target detection using the BOF technique. The features corresponding to the targets are matched to those in the subsequent frame for target tracking purposes with a set of heuristic rules. Tracking performance is compared with an optical-flow-based method with respect to the ground truth target positions for different real IR and VIS band videos and synthetic IR videos. Scenarios are composed of videos recorded/generated at different times of day, containing single and multiple targets located at different ranges and orientations. The experimental results show that sea-surface targets can be detected and tracked with plausible accuracies by using the BOF technique with the SIFT in both IR and VIS band videos.

© 2011 Optical Society of America

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2011 (1)

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

2009 (3)

J. F. Khan, M. S. Alam, and S. M. A. Bhuiyan, “Automatic target detection in forward-looking infrared imagery via probabilistic neural networks,” Appl. Opt. 48, 464–476(2009).
[CrossRef]

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

2008 (1)

C. Park, K. Baea, and J.-H. Jung, “Object recognition in infrared image sequences using scale invariant feature transform,” Proc. SPIE , 6968, 69681P (2008).
[CrossRef]

2006 (1)

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

2005 (1)

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846–1852(2005).
[CrossRef]

2004 (5)

2003 (1)

2001 (2)

T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” Int. J. Comput. Vis. 43, 29–44 (2001).
[CrossRef]

M. Diani, A. Baldacci, and G. Corsini, “Novel background removal algorithm for Navy infrared search and track systems,” Opt. Eng. 40, 1729–1734 (2001).
[CrossRef]

2000 (1)

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

1999 (1)

1997 (1)

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

1991 (1)

J. D. Agostino and C. Webb, “Three-dimensional analysis framework and measurement methodology for imaging system noise,” Proc. SPIE , 1488, 110–121 (1991).
[CrossRef]

Agostino, J. D.

J. D. Agostino and C. Webb, “Three-dimensional analysis framework and measurement methodology for imaging system noise,” Proc. SPIE , 1488, 110–121 (1991).
[CrossRef]

Alam, M. S.

Angell, C. R.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Aytaç, T.

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

Baea, K.

C. Park, K. Baea, and J.-H. Jung, “Object recognition in infrared image sequences using scale invariant feature transform,” Proc. SPIE , 6968, 69681P (2008).
[CrossRef]

Bal, A.

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846–1852(2005).
[CrossRef]

A. Bal and M. S. Alam, “Dynamic target tracking with fringe-adjusted joint transform correlation and template matching,” Appl. Opt. 43, 4874–4881 (2004).
[CrossRef]

Baldacci, A.

M. Diani, A. Baldacci, and G. Corsini, “Novel background removal algorithm for Navy infrared search and track systems,” Opt. Eng. 40, 1729–1734 (2001).
[CrossRef]

Bernhardt, M.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Bhuiyan, S. M. A.

Bouguet, J.-Y.

J.-Y. Bouguet, “Pyramidal implementation of the Lucas–Kanade feature tracker,” Technical report, Intel Corp., Microprocessor Research Labs, 1999.

Bouma, H.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

Bray, C.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

Çakir, S.

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

Camargo, A.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

Chen, H.-W.

Corsini, G.

M. Diani, A. Baldacci, and G. Corsini, “Novel background removal algorithm for Navy infrared search and track systems,” Opt. Eng. 40, 1729–1734 (2001).
[CrossRef]

Criminisi, A.

J. Winn, A. Criminisi, and T. Minka, “Object categorization by learned universal visual dictionary,” in Proceedings of the Tenth IEEE International Conference on Computer Vision (IEEE, 2005), Vol.  2, pp. 1800–1807.

Csurka, G.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

Dance, C. R.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

de Groot, J. F.

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

de Visser, M.

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

den Hollander, R. J. M.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

Diani, M.

M. Diani, A. Baldacci, and G. Corsini, “Novel background removal algorithm for Navy infrared search and track systems,” Opt. Eng. 40, 1729–1734 (2001).
[CrossRef]

Ding, M.-Y.

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

Fan, L.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

Fevig, R.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

Gerek, Ö. Nezih

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

Geusebroek, J. M.

J. C. van Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel codebooks for scene categorization,” in Lecture Notes in Computer Science: European Conference on Computer Vision (Springer, 2008), pp. 696–709.
[CrossRef]

Gong, L.-Q.

J.-Z. Liu, X.-C. Yu, L.-Q. Gong, and W.-S. Yu, “Automatic matching of infrared image sequences based on rotation invariant,” in Proceedings of the IEEE International Conference on Environmental Science and Information Technology (IEEE, 2009), pp. 365–368.

Harris, C.

C. Harris and M. Stephens, “A combined corner and edge detection,” in Proceedings of The Fourth Alvey Vision Conference (The British Machine Vision Association and Society for Pattern Recognition, 1988), Vol.  15, pp. 147–151.

Hendriks, E. A.

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

Heo, P. G.

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Hickman, D.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Hughes, H. G.

Iftekharuddin, K. M.

Jung, J.-H.

C. Park, K. Baea, and J.-H. Jung, “Object recognition in infrared image sequences using scale invariant feature transform,” Proc. SPIE , 6968, 69681P (2008).
[CrossRef]

Jurie, F.

E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification,” in Proceedings of the European Conference on Computer Vision (Springer, 2006), pp. 490–503.

Kalviainen, H.

T. Kinnunen, J. K. Kamarainen, L. Lensu, and H. Kalviainen, “Bag-of-features codebook generation by self-organisation,” in Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (Springer-Verlag, 2009), pp. 124–132.

Kamarainen, J. K.

T. Kinnunen, J. K. Kamarainen, L. Lensu, and H. Kalviainen, “Bag-of-features codebook generation by self-organisation,” in Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (Springer-Verlag, 2009), pp. 124–132.

Kanade, T.

B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1981), Vol.  2, pp. 674–679.

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Technical report (Carnegie Mellon Univ., 1991).

Kemp, R. A. W.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

Khan, J. F.

Kinnunen, T.

T. Kinnunen, J. K. Kamarainen, L. Lensu, and H. Kalviainen, “Bag-of-features codebook generation by self-organisation,” in Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (Springer-Verlag, 2009), pp. 124–132.

Lee, H.

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Lensen, H. A.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

Lensu, L.

T. Kinnunen, J. K. Kamarainen, L. Lensu, and H. Kalviainen, “Bag-of-features codebook generation by self-organisation,” in Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (Springer-Verlag, 2009), pp. 124–132.

Leung, T.

T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” Int. J. Comput. Vis. 43, 29–44 (2001).
[CrossRef]

Littfin, K. M.

Liu, J.-Z.

J.-Z. Liu, X.-C. Yu, L.-Q. Gong, and W.-S. Yu, “Automatic matching of infrared image sequences based on rotation invariant,” in Proceedings of the IEEE International Conference on Environmental Science and Information Technology (IEEE, 2009), pp. 365–368.

Lowe, D. G.

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

Lucas, B. D.

B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1981), Vol.  2, pp. 674–679.

Malik, J.

T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” Int. J. Comput. Vis. 43, 29–44 (2001).
[CrossRef]

Martel, F.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

McGrath, C. P.

Mendlovic, D.

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

Minka, T.

J. Winn, A. Criminisi, and T. Minka, “Object categorization by learned universal visual dictionary,” in Proceedings of the Tenth IEEE International Conference on Computer Vision (IEEE, 2005), Vol.  2, pp. 1800–1807.

Moravec, H. P.

H. P. Moravec, “Visual mapping by a robot rover,” in Proceedings of the 6th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1979), Vol.  1, pp. 598–600.

Nowak, E.

E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification,” in Proceedings of the European Conference on Computer Vision (Springer, 2006), pp. 490–503.

Olson, T.

Park, C.

C. Park, K. Baea, and J.-H. Jung, “Object recognition in infrared image sequences using scale invariant feature transform,” Proc. SPIE , 6968, 69681P (2008).
[CrossRef]

Park, H.

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Patel, D.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Peng, J.-X.

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

Perona, P.

M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” in Proceedings of the European Conference on Computer Vision (Springer, 2000), pp. 18–32.

Rivlin, E.

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

Rotman, S.

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

Sadjadi, F. A.

Schultz, R. R.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

Schwering, P. B. W.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

Shaik, J. S.

Shi, J.

J. Shi and C. Tomasi, “Good features to track,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1994), pp. 593–600.

Smeulders, A. W. M.

J. C. van Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel codebooks for scene categorization,” in Lecture Notes in Computer Science: European Conference on Computer Vision (Springer, 2008), pp. 696–709.
[CrossRef]

Smith, M. I.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Stephens, M.

C. Harris and M. Stephens, “A combined corner and edge detection,” in Proceedings of The Fourth Alvey Vision Conference (The British Machine Vision Association and Society for Pattern Recognition, 1988), Vol.  15, pp. 147–151.

Suk, J.-Y.

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Sutha, S.

Tomasi, C.

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Technical report (Carnegie Mellon Univ., 1991).

J. Shi and C. Tomasi, “Good features to track,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1994), pp. 593–600.

Triggs, B.

E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification,” in Proceedings of the European Conference on Computer Vision (Springer, 2006), pp. 490–503.

van den Broek, S. P.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
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van der Mark, W.

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

van Gemert, J. C.

J. C. van Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel codebooks for scene categorization,” in Lecture Notes in Computer Science: European Conference on Computer Vision (Springer, 2008), pp. 696–709.
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Vedaldi, A.

A. Vedaldi, www.vlfeat.org/vedaldi/code/sift.html, SIFT (2010).

Veenman, C. J.

J. C. van Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel codebooks for scene categorization,” in Lecture Notes in Computer Science: European Conference on Computer Vision (Springer, 2008), pp. 696–709.
[CrossRef]

Wang, Y.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

Webb, C.

J. D. Agostino and C. Webb, “Three-dimensional analysis framework and measurement methodology for imaging system noise,” Proc. SPIE , 1488, 110–121 (1991).
[CrossRef]

Weber, M.

M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” in Proceedings of the European Conference on Computer Vision (Springer, 2000), pp. 18–32.

Welling, M.

M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” in Proceedings of the European Conference on Computer Vision (Springer, 2000), pp. 18–32.

Whitehead, P.

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

Willamowski, J.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

Winn, J.

J. Winn, A. Criminisi, and T. Minka, “Object categorization by learned universal visual dictionary,” in Proceedings of the Tenth IEEE International Conference on Computer Vision (IEEE, 2005), Vol.  2, pp. 1800–1807.

Xiong, Y.

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

Xue, D.-H.

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

Yeou, B.-Y.

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Yildirim, A.

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

Yu, W.-S.

J.-Z. Liu, X.-C. Yu, L.-Q. Gong, and W.-S. Yu, “Automatic matching of infrared image sequences based on rotation invariant,” in Proceedings of the IEEE International Conference on Environmental Science and Information Technology (IEEE, 2009), pp. 365–368.

Yu, X.-C.

J.-Z. Liu, X.-C. Yu, L.-Q. Gong, and W.-S. Yu, “Automatic matching of infrared image sequences based on rotation invariant,” in Proceedings of the IEEE International Conference on Environmental Science and Information Technology (IEEE, 2009), pp. 365–368.

Zalevsky, Z.

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

Zeisse, C. R.

Appl. Opt. (5)

IEEE Trans. Aerospace Electron. Syst. (1)

Y. Xiong, J.-X. Peng, M.-Y. Ding, and D.-H. Xue, “An extended track-before-detect algorithm for infrared target detection,” IEEE Trans. Aerospace Electron. Syst. 33, 1087–1092 (1997).
[CrossRef]

IEEE Trans. Instrum. Meas. (1)

A. Bal and M. S. Alam, “Automatic target tracking in FLIR image sequences using intensity variation function and template modeling,” IEEE Trans. Instrum. Meas. 54, 1846–1852(2005).
[CrossRef]

Int. J. Comput. Vis. (2)

D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004).
[CrossRef]

T. Leung and J. Malik, “Representing and recognizing the visual appearance of materials using three-dimensional textons,” Int. J. Comput. Vis. 43, 29–44 (2001).
[CrossRef]

J. Opt. Soc. Am. A (1)

Opt. Eng. (5)

M. Diani, A. Baldacci, and G. Corsini, “Novel background removal algorithm for Navy infrared search and track systems,” Opt. Eng. 40, 1729–1734 (2001).
[CrossRef]

M. de Visser, P. B. W. Schwering, J. F. de Groot, and E. A. Hendriks, “Passive ranging using an infrared search and track sensor,” Opt. Eng. 45, 1–14 (2006).
[CrossRef]

S. Çakır, T. Aytaç, A. Yıldırım, and Ö. Nezih Gerek, “Classifier-based offline feature selection and evaluation for visual tracking of sea-surface and aerial targets,” Opt. Eng. 50, 107205 (2011).
[CrossRef]

Z. Zalevsky, D. Mendlovic, E. Rivlin, and S. Rotman, “Contrasted statistical processing algorithm for obtaining improved target detection performances in infrared cluttered environment,” Opt. Eng. 39, 2609–2617(2000).
[CrossRef]

H. Lee, P. G. Heo, J.-Y. Suk, B.-Y. Yeou, and H. Park, “Scale-invariant object tracking method using strong corners in the scale domain,” Opt. Eng. 48, 017204 (2009).
[CrossRef]

Proc. SPIE (4)

P. B. W. Schwering, H. A. Lensen, S. P. van den Broek, R. J. M. den Hollander, W. van der Mark, H. Bouma, and R. A. W. Kemp, “Application of heterogeneous multiple camera system with panoramic capabilities in a harbor environment,” Proc. SPIE , 7481, 74810C (2009).
[CrossRef]

M. I. Smith, M. Bernhardt, C. R. Angell, D. Hickman, P. Whitehead, and D. Patel, “Validation and acceptance of synthetic infrared imagery,” Proc. SPIE , 5408, 9–21 (2004).
[CrossRef]

J. D. Agostino and C. Webb, “Three-dimensional analysis framework and measurement methodology for imaging system noise,” Proc. SPIE , 1488, 110–121 (1991).
[CrossRef]

C. Park, K. Baea, and J.-H. Jung, “Object recognition in infrared image sequences using scale invariant feature transform,” Proc. SPIE , 6968, 69681P (2008).
[CrossRef]

Other (15)

M. Weber, M. Welling, and P. Perona, “Unsupervised learning of models for recognition,” in Proceedings of the European Conference on Computer Vision (Springer, 2000), pp. 18–32.

J. Winn, A. Criminisi, and T. Minka, “Object categorization by learned universal visual dictionary,” in Proceedings of the Tenth IEEE International Conference on Computer Vision (IEEE, 2005), Vol.  2, pp. 1800–1807.

A. Vedaldi, www.vlfeat.org/vedaldi/code/sift.html, SIFT (2010).

B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the 7th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1981), Vol.  2, pp. 674–679.

J. Shi and C. Tomasi, “Good features to track,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 1994), pp. 593–600.

J.-Y. Bouguet, “Pyramidal implementation of the Lucas–Kanade feature tracker,” Technical report, Intel Corp., Microprocessor Research Labs, 1999.

H. P. Moravec, “Visual mapping by a robot rover,” in Proceedings of the 6th International Joint Conference on Artificial Intelligence (Morgan Kaufmann, 1979), Vol.  1, pp. 598–600.

C. Harris and M. Stephens, “A combined corner and edge detection,” in Proceedings of The Fourth Alvey Vision Conference (The British Machine Vision Association and Society for Pattern Recognition, 1988), Vol.  15, pp. 147–151.

C. Tomasi and T. Kanade, “Detection and tracking of point features,” Technical report (Carnegie Mellon Univ., 1991).

J.-Z. Liu, X.-C. Yu, L.-Q. Gong, and W.-S. Yu, “Automatic matching of infrared image sequences based on rotation invariant,” in Proceedings of the IEEE International Conference on Environmental Science and Information Technology (IEEE, 2009), pp. 365–368.

Y. Wang, A. Camargo, R. Fevig, F. Martel, and R. R. Schultz, “Image mosaicking from uncooled thermal IR video captured by a small UAV,” in Proceedings of the IEEE Southwest Symposium on Image Analysis and Interpretation (IEEE, 2008), pp. 161–164.

G. Csurka, C. R. Dance, L. Fan, J. Willamowski, and C. Bray, “Visual categorization with bags of keypoints,” in Proceedings of the European Conference on Computer Vision Workshop on Statistical Learning in Computer Vision (Springer, 2004), pp. 59–74.

T. Kinnunen, J. K. Kamarainen, L. Lensu, and H. Kalviainen, “Bag-of-features codebook generation by self-organisation,” in Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps (Springer-Verlag, 2009), pp. 124–132.

J. C. van Gemert, J. M. Geusebroek, C. J. Veenman, and A. W. M. Smeulders, “Kernel codebooks for scene categorization,” in Lecture Notes in Computer Science: European Conference on Computer Vision (Springer, 2008), pp. 696–709.
[CrossRef]

E. Nowak, F. Jurie, and B. Triggs, “Sampling strategies for bag-of-features image classification,” in Proceedings of the European Conference on Computer Vision (Springer, 2006), pp. 490–503.

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Figures (10)

Fig. 1
Fig. 1

Flow diagram of the BOF procedure.

Fig. 2
Fig. 2

Detected horizon is marked with a solid white line.

Fig. 3
Fig. 3

Classification results: circles and plus signs indicate the target and background SIFT descriptor locations, respectively.

Fig. 4
Fig. 4

SIFT match sample between two consecutive frames.

Fig. 5
Fig. 5

The rectangle represented by ABCD is the target area, and EFGH is the detected area. J represents the center of the ABCD rectangle, and K represents the center of the EFGH rectangle. O corresponds to the origin of the coordinate system.

Fig. 6
Fig. 6

Ground truth and the detected target areas for a sample frame. The ground truth is marked with the solid rectangle. The detected region is marked with the dashed lines.

Fig. 7
Fig. 7

Tracking results from a real IR video.

Fig. 8
Fig. 8

M 3 , M 4 , and detection results for a sample real IR video sequence.

Fig. 9
Fig. 9

Tracking results from synthetic IR video.

Fig. 10
Fig. 10

Tracking results from VIS band videos.

Tables (5)

Tables Icon

Table 1 Real IR Video Results by Using Centroids Extracted from the Real Videos a

Tables Icon

Table 2 Real IR Video Results by Using Centroids Extracted from the Synthetic IR Videos

Tables Icon

Table 3 Synthetic IR Video Results by Using Centroid Extracted from the Synthetic Videos a

Tables Icon

Table 4 Synthetic IR Video Results by Using Centroids Extracted from Real Videos

Tables Icon

Table 5 Visual Band Video Results a

Equations (13)

Equations on this page are rendered with MathJax. Learn more.

D G ( x , y , σ ) = [ G ( x , y , n σ ) G ( x , y , σ ) ] * I ( x , y ) .
H = I x x I x y I x y I y y .
( α + β ) 2 α β ( r + 1 ) 2 r .
D x ( y ) = I ( x , y ) I ( x + 1 , y ) ,
dist j k t , b = n = 1 p [ desc j ( n ) cent k t , b ( n ) ] 2 ,
dist j k norm t , b = dist j k t , b max [ dist j k t , b ] ,
decision = { target , min [ dist j k norm t ] min [ dist j k norm b ] , background , otherwise .
Δ ( m ) = m y i m y i + 1 ,
decision ( m ) { true , μ s σ s < Δ ( m ) < μ s + σ s false , otherwise .
L ( m ) = m x i m x i + 1 + w .
decision ( m ) { true , μ l σ l < L ( m ) < μ l + σ l false , otherwise ,
ROI_Validity = { invalid w diff / w max > T or h diff / h max > T valid otherwise ,
ROI = ROI prev + ROI cur 2 ,

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