Abstract

In this paper, we introduce a novel surveillance system based on thermal catadioptric omnidirectional (TCO) vision. The conventional contour-based methods are difficult to be applied to the TCO sensor for detection or tracking purposes due to the distortion of TCO vision. To solve this problem, we propose a contour coding based rotating adaptive model (RAM) that can extract the contour feature from the TCO vision directly as it takes advantage of the relative angle based on the characteristics of TCO vision to change the sequence of sampling automatically. A series of experiments and quantitative analyses verify that the performance of the proposed RAM-based contour coding feature for human detection and tracking are satisfactory in TCO vision.

© 2012 Optical Society of America

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References

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  1. P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2003), pp. 734–741.
  2. J. Burchett, M. Shankar, A. Hamza, B. Guenther, N. Pitsianis, and D. Brady, “Lightweight biometric detection system for human classification using pyroelectric infrared detectors,” Appl. Opt. 45, 3031–3037 (2006).
    [CrossRef]
  3. R. Liu, E. Liu, J. Yang, Y. Zeng, F. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga–Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780–7791 (2007).
    [CrossRef]
  4. A. Abdi, M. Schmiedekamp, and S. Phoha, “Probabilistic color matching and tracking of human subjects,” Appl. Opt. 49, 4926–4935 (2010).
    [CrossRef]
  5. F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
    [CrossRef]
  6. M. Vollmer and K. P. Mollmann, “Selected applications in other fields,” in Infrared Thermal Imaging: Fundamentals, Research and Applications (Wiley-Vch, 2010), pp. 566–579.
  7. X. Wang, L. Liu, and Z. Tang, “Infrared human tracking with improved mean shift algorithm based on multicue fusion,” Appl. Opt. 48, 4201–4212 (2009).
    [CrossRef]
  8. J. Davis and M. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of IEEE Workshop on Applications of Computer Vision (IEEE, 2005), pp. 364–369.
  9. F. Suard, A. Rakotomamonjy, and A. Bensrhair, “Pedestrian detection using infrared images and histograms of oriented gradients,” in Proceedings of IEEE Intelligent Vehicles Symposium (IEEE, 2006), pp. 206–212.
  10. N. Dalal and B. Triggs, “Histogram of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 886–893.
  11. C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
    [CrossRef]
  12. S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
    [CrossRef]
  13. T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
    [CrossRef]
  14. J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.
  15. O. Jaime and B. Eduardo, “Omnidirectional vision tracking with particle filter,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 1115–1118.
  16. W. Schulz, M. Enzwiler, and T. Ehlgen, “Pedestrian recognition from a moving catadioptric camera,” in Proceedings of the 29th DAGM conference on Pattern Recognition (Academic, 2007), pp. 456–465.
  17. A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).
  18. Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.
  19. W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.
  20. C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis. 38, 15–33 (2000).
    [CrossRef]
  21. V. N. Vapnik, The Nature of Statistical Learning Theory(Academic, 1995).
  22. C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Disc. 2, 121–167 (1998).
    [CrossRef]
  23. V. N. Vapnik, Statistical Learning Theory (Academic, 1998).
  24. J. Platt, “Probabilities for SV machines,” in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, eds. (Academic, 2000), pp. 61–74.
  25. M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
    [CrossRef]
  26. A. Doucet, J. de Freitas, and N. J. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, eds. (Academic, 2001), pp. 3–14.
  27. M. Isard and A. Blake, “Condensation-conditional desity propagation for visual tracking,” Int. J. Comput. Vis. 29, 5–28 (1998).
    [CrossRef]

2010

2009

X. Wang, L. Liu, and Z. Tang, “Infrared human tracking with improved mean shift algorithm based on multicue fusion,” Appl. Opt. 48, 4201–4212 (2009).
[CrossRef]

S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
[CrossRef]

A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).

2007

C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
[CrossRef]

R. Liu, E. Liu, J. Yang, Y. Zeng, F. Wang, and Y. Cao, “Automatically detect and track infrared small targets with kernel Fukunaga–Koontz transform and Kalman prediction,” Appl. Opt. 46, 7780–7791 (2007).
[CrossRef]

2006

2005

F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
[CrossRef]

2004

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

2002

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

2000

C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis. 38, 15–33 (2000).
[CrossRef]

1998

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Disc. 2, 121–167 (1998).
[CrossRef]

M. Isard and A. Blake, “Condensation-conditional desity propagation for visual tracking,” Int. J. Comput. Vis. 29, 5–28 (1998).
[CrossRef]

Abdi, A.

Arulampalam, M.

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

Bai, T.

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

Barczak, A.

A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).

Bazin, J.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

Bensrhair, A.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, “Pedestrian detection using infrared images and histograms of oriented gradients,” in Proceedings of IEEE Intelligent Vehicles Symposium (IEEE, 2006), pp. 206–212.

Blake, A.

M. Isard and A. Blake, “Condensation-conditional desity propagation for visual tracking,” Int. J. Comput. Vis. 29, 5–28 (1998).
[CrossRef]

Boult, T.

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

Brady, D.

Burchett, J.

Burges, C. J. C.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Disc. 2, 121–167 (1998).
[CrossRef]

Cao, Y.

Clapp, T.

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

Dai, C.

C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
[CrossRef]

Dalal, N.

N. Dalal and B. Triggs, “Histogram of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 886–893.

Davis, J.

J. Davis and M. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of IEEE Workshop on Applications of Computer Vision (IEEE, 2005), pp. 364–369.

de Freitas, J.

A. Doucet, J. de Freitas, and N. J. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, eds. (Academic, 2001), pp. 3–14.

Demonceaux, C.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

Doucet, A.

A. Doucet, J. de Freitas, and N. J. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, eds. (Academic, 2001), pp. 3–14.

Eckmann, M.

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

Eduardo, B.

O. Jaime and B. Eduardo, “Omnidirectional vision tracking with particle filter,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 1115–1118.

Ehlgen, T.

W. Schulz, M. Enzwiler, and T. Ehlgen, “Pedestrian recognition from a moving catadioptric camera,” in Proceedings of the 29th DAGM conference on Pattern Recognition (Academic, 2007), pp. 456–465.

Enzwiler, M.

W. Schulz, M. Enzwiler, and T. Ehlgen, “Pedestrian recognition from a moving catadioptric camera,” in Proceedings of the 29th DAGM conference on Pattern Recognition (Academic, 2007), pp. 456–465.

Fujimura, K.

F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
[CrossRef]

Gao, M.

W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.

Gao, X.

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

Gordon, N.

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

Gordon, N. J.

A. Doucet, J. de Freitas, and N. J. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, eds. (Academic, 2001), pp. 3–14.

Grassi, V.

A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).

Guenther, B.

Hamza, A.

Isard, M.

M. Isard and A. Blake, “Condensation-conditional desity propagation for visual tracking,” Int. J. Comput. Vis. 29, 5–28 (1998).
[CrossRef]

Jaime, O.

O. Jaime and B. Eduardo, “Omnidirectional vision tracking with particle filter,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 1115–1118.

Jones, M.

P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2003), pp. 734–741.

Keck, M.

J. Davis and M. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of IEEE Workshop on Applications of Computer Vision (IEEE, 2005), pp. 364–369.

Kweon, I.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

Li, X.

C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
[CrossRef]

Li, Y.

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

Li, Z.

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

Liu, E.

Liu, H.

W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.

Liu, L.

Liu, R.

Liu, X.

F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
[CrossRef]

Maskell, S.

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

Micheals, R.

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

Min, G.

S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
[CrossRef]

Mollmann, K. P.

M. Vollmer and K. P. Mollmann, “Selected applications in other fields,” in Infrared Thermal Imaging: Fundamentals, Research and Applications (Wiley-Vch, 2010), pp. 566–579.

Okamoto, J.

A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).

Papageorgiou, C.

C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis. 38, 15–33 (2000).
[CrossRef]

Phoha, S.

Pitsianis, N.

Platt, J.

J. Platt, “Probabilities for SV machines,” in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, eds. (Academic, 2000), pp. 61–74.

Poggio, T.

C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis. 38, 15–33 (2000).
[CrossRef]

Rakotomamonjy, A.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, “Pedestrian detection using infrared images and histograms of oriented gradients,” in Proceedings of IEEE Intelligent Vehicles Symposium (IEEE, 2006), pp. 206–212.

Schmiedekamp, M.

Schulz, W.

W. Schulz, M. Enzwiler, and T. Ehlgen, “Pedestrian recognition from a moving catadioptric camera,” in Proceedings of the 29th DAGM conference on Pattern Recognition (Academic, 2007), pp. 456–465.

Shankar, M.

Snow, D.

P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2003), pp. 734–741.

Suard, F.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, “Pedestrian detection using infrared images and histograms of oriented gradients,” in Proceedings of IEEE Intelligent Vehicles Symposium (IEEE, 2006), pp. 206–212.

Sun, F.

W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.

Tang, Y.

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

Tang, Z.

Triggs, B.

N. Dalal and B. Triggs, “Histogram of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 886–893.

Vapnik, V. N.

V. N. Vapnik, The Nature of Statistical Learning Theory(Academic, 1995).

V. N. Vapnik, Statistical Learning Theory (Academic, 1998).

Vasseur, P.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

Viola, P.

P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2003), pp. 734–741.

Vollmer, M.

M. Vollmer and K. P. Mollmann, “Selected applications in other fields,” in Infrared Thermal Imaging: Fundamentals, Research and Applications (Wiley-Vch, 2010), pp. 566–579.

Wang, F.

Wang, X.

Xu, F.

F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
[CrossRef]

Yang, J.

Yang, S.

S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
[CrossRef]

Ye, W.

W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.

Yoon, K.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

Zeng, Y.

Zhang, C.

S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
[CrossRef]

Zheng, Y.

C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
[CrossRef]

Zhou, X.

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

Appl. Opt.

Comput. Vis. Image Und.

C. Dai, Y. Zheng, and X. Li, “Layered representation for pedestrian detection and tracking in infrared imagery,” Comput. Vis. Image Und. 106, 288–299 (2007).
[CrossRef]

Data Min. Knowl. Disc.

C. J. C. Burges, “A tutorial on support vector machines for pattern recognition,” Data Min. Knowl. Disc. 2, 121–167 (1998).
[CrossRef]

IEEE Trans. Intell. Transport. Syst.

F. Xu, X. Liu, and K. Fujimura, “Pedestrian detection and tracking with night vision,” IEEE Trans. Intell. Transport. Syst. 6, 63–71 (2005).
[CrossRef]

IEEE Trans. Signal Process.

M. Arulampalam, S. Maskell, N. Gordon, and T. Clapp, “A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking,” IEEE Trans. Signal Process. 50, 174–188 (2002).
[CrossRef]

Image Vis. Comput.

T. Boult, X. Gao, R. Micheals, and M. Eckmann, “Omni-directional visual surveillance,” Image Vis. Comput. 22, 515–534 (2004).
[CrossRef]

Int. J. Comput. Vis.

M. Isard and A. Blake, “Condensation-conditional desity propagation for visual tracking,” Int. J. Comput. Vis. 29, 5–28 (1998).
[CrossRef]

C. Papageorgiou and T. Poggio, “A trainable system for object detection,” Int. J. Comput. Vis. 38, 15–33 (2000).
[CrossRef]

Res. Lett. Inf. Math. Sci.

A. Barczak, J. Okamoto, and V. Grassi, “Face tracking using a hyperbolic catadioptric omnidirectional system,” Res. Lett. Inf. Math. Sci. 13, 55–67 (2009).

Vis. Res.

S. Yang, G. Min, and C. Zhang, “Tracking unknown moving targets on omnidirectional vision,” Vis. Res. 49, 362–367 (2009).
[CrossRef]

Other

Y. Tang, Y. Li, T. Bai, X. Zhou, and Z. Li, “Human tracking in thermal catadioptric omnidirectional vision,” in Proceedings of IEEE International Conference on Information and Automation (IEEE, 2011), pp. 97–102.

W. Ye, H. Liu, F. Sun, and M. Gao, “Vehicle tracking based on co-learning particle filter,” in Proceedings of IEEE International Conference on Intelligent Robots and System (IEEE, 2009), pp. 2979–2984.

J. Bazin, K. Yoon, I. Kweon, C. Demonceaux, and P. Vasseur, “Particle filter approach adapted to catadioptric images for target tracking application,” in Proceedings of British Machine Vision Conference (Academic, 2009), pp. 1–15.

O. Jaime and B. Eduardo, “Omnidirectional vision tracking with particle filter,” in Proceedings of International Conference on Pattern Recognition (IEEE, 2006) pp. 1115–1118.

W. Schulz, M. Enzwiler, and T. Ehlgen, “Pedestrian recognition from a moving catadioptric camera,” in Proceedings of the 29th DAGM conference on Pattern Recognition (Academic, 2007), pp. 456–465.

M. Vollmer and K. P. Mollmann, “Selected applications in other fields,” in Infrared Thermal Imaging: Fundamentals, Research and Applications (Wiley-Vch, 2010), pp. 566–579.

J. Davis and M. Keck, “A two-stage template approach to person detection in thermal imagery,” in Proceedings of IEEE Workshop on Applications of Computer Vision (IEEE, 2005), pp. 364–369.

F. Suard, A. Rakotomamonjy, and A. Bensrhair, “Pedestrian detection using infrared images and histograms of oriented gradients,” in Proceedings of IEEE Intelligent Vehicles Symposium (IEEE, 2006), pp. 206–212.

N. Dalal and B. Triggs, “Histogram of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 886–893.

V. N. Vapnik, The Nature of Statistical Learning Theory(Academic, 1995).

V. N. Vapnik, Statistical Learning Theory (Academic, 1998).

J. Platt, “Probabilities for SV machines,” in Advances in Large Margin Classifiers, A. J. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans, eds. (Academic, 2000), pp. 61–74.

P. Viola, M. Jones, and D. Snow, “Detecting pedestrians using patterns of motion and appearance,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2003), pp. 734–741.

A. Doucet, J. de Freitas, and N. J. Gordon, “An introduction to sequential Monte Carlo methods,” in Sequential Monte Carlo Methods in Practice, A. Doucet, J. F. G. de Freitas, and N. J. Gordon, eds. (Academic, 2001), pp. 3–14.

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Figures (12)

Fig. 1.
Fig. 1.

Representative image of TCO vision.

Fig. 2.
Fig. 2.

Principle of proposed contour coding based RAM.

Fig. 3.
Fig. 3.

Template of contour coding based rotating adaptive feature.

Fig. 4.
Fig. 4.

Haar wavelet framework in omnidirectional vision. (a) Haar scaling function and wavelet and (b) three types of RAM-based 2D Haar wavelets in omnidirectional vision.

Fig. 5.
Fig. 5.

Working principle of transformation algorithm of gradient orientation in RAM.

Fig. 6.
Fig. 6.

Configuration of TCO surveillance system.

Fig. 7.
Fig. 7.

Representatives of extracted TCO samples.

Fig. 8.
Fig. 8.

Performance of the RAM/CT-gradient, RAM/CT-Haar wavelet, and RAM/CT-HOG.

Fig. 9.
Fig. 9.

Representatives of the human detection on TCO vision database with RAM-feature (RAM-HOG).

Fig. 10.
Fig. 10.

Experiment for human detection in TCO vision with polarity switch.

Fig. 11.
Fig. 11.

RMSE of RAM/CT-feature-PFs with the different number of particles. (a) RAM-Gradient-PF and CT-Gradient-PF. (b) RAM-Haar-wavelet-PF and CT-Haar-wavelet-PF. (c) RAM-HOG-PF and CT-HOG-PF. (d) RAM-Gradient-PF, RAM-Haar-wavelet-PF, RAM-HOG-PF, and G-PF.

Fig. 12.
Fig. 12.

Experiments for human tracking in TCO vision. From left to right they are G-PF, RAM-G-PF, RAM-Haar-wavelet-PF, and RAM-HOG-PF. Experiment I: forenoon, experiment II: noon, experiment III, IV: night.

Tables (1)

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Table 1. Detection Results of RAM-Gradient (RAM-G), Haar Wavelet (RAM-HW), and HOG (RMA-HOG) in TCO Vision Database (TP, True Positive; FP, False Positive; Sensitivity, Hit Rate)

Equations (10)

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ψ ( t ) = { 1 0 t < 1 / 2 1 1 / 2 t < 1 0 otherwise .
ϕ ( t ) = { 1 0 t < 1 0 otherwise .
g ( x , y ) = ( I ( x , y ) I ( x 1 , y ) ) 2 + ( I ( x , y ) I ( x , y 1 ) ) 2 ,
θ ( x , y ) = tan 1 ( ( I ( x , y ) I ( x , y 1 ) ) / ( I ( x , y ) I ( x 1 , y ) ) ) .
min J ( w , b , ξ i ) = 1 2 w T w + C i = 1 N ξ i .
y i [ w T φ ( x i ) + b ] 1 ξ i , ξ 0 ,
max i = 1 l α i 1 2 i = 1 , j = 1 l α i α j y i y j k ( x i , x ) ,
d = 1 q ,
p ( z k | x k i ) exp ( λ · d 2 ) ,
w k i w k 1 i p ( z k | x k i ) .

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