Visual Object Tracking in 3D with Color Based Particle Filter
This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.
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projective vision, Proc. of Int. Conf. on Vision Interface, pp. 383-390,
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with wide-angle vision, 5th IFAC/EURON Symposium on Intelligent
Autonomous Vehicles, July 2004.
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from a sequence of images. Proc. of Int. Conf. on Machine Learning, pp.
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particle filters for on-line non-linear/non-gaussian bayesian tracking,
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Genetic Programming and Evolvable Machines, vol. 2, pp. 101-109, 2001
[8] M. Isard and A. Blake, CONDENSATION- conditional density propagation
for visual tracking, Int. Journal of Computer Vision, vol. 20, no. 1,
pp. 5-28, 1998.
[9] P. Perez, J. Vermaak and A. Blake, Data fusion for visual tracking with
particles, Proceedings of IEEE, vol. 92, no. 3, pp. 495-513, March 2004.
[10] D. Mackay, Introduction to Monte Carlo methods, In M. Jordan editor,
Learning in graphical models, pp. 175-204, MIT Press, 1999
[11] A. J. Davison, Real-time simultaneous localisation and mapping with
a single camera, IEEE Int. Conf. on Computer Vision, ICCV-2003, pp.
1403-1410, Nice (France), October 2003.
[12] R.T. Collins, Multi-image focus of attention for rapid site model construction,
IEEE Int. Conf. on Computer Vision and Pattern Recognition,
1997, pp. 575-581, San Juan, Puerto Rico, June 1007.
[1] D. Gorodnichy, S. Malik and G. Roth, Affordable 3D face tracking using
projective vision, Proc. of Int. Conf. on Vision Interface, pp. 383-390,
Calgary (Canada) May 2002.
[2] A.J. Davison, Y. Gonzalez-Cid and N. Kita, Real-time 3D SLAM
with wide-angle vision, 5th IFAC/EURON Symposium on Intelligent
Autonomous Vehicles, July 2004.
[3] D. Margaritis and S. Thrun, Learning to locate an object in 3D space
from a sequence of images. Proc. of Int. Conf. on Machine Learning, pp.
332-340, 1998.
[4] D. Fox, W. Burgard, F. Dellaert and S. Thrun, Monte Carlo localization:
efficient position estimation for mobile robots, In Proc. of 16th. AAAI
Nat. Conf. on Artificial Intelligenge, pp. 343-349, Orlando (USA), July
1999
[5] S. Arulampalam, S. Maskell, N. Gordon and T. Clapp, A tutorial on
particle filters for on-line non-linear/non-gaussian bayesian tracking,
IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188,
2002.
[6] A.M. Boumaza and J. Louchet, Mobile robot sensor fusion using flies
In G├╝nter Raidl et.al. editors, Applications of Evolutionary Computing,
Evo Workshops 2003, Lecture Notes in Computer Science, vol. 2611, pp.
357-367, Springer, 2003.
[7] Jean Louchet, Using an individual evolution strategy for stereovision,
Genetic Programming and Evolvable Machines, vol. 2, pp. 101-109, 2001
[8] M. Isard and A. Blake, CONDENSATION- conditional density propagation
for visual tracking, Int. Journal of Computer Vision, vol. 20, no. 1,
pp. 5-28, 1998.
[9] P. Perez, J. Vermaak and A. Blake, Data fusion for visual tracking with
particles, Proceedings of IEEE, vol. 92, no. 3, pp. 495-513, March 2004.
[10] D. Mackay, Introduction to Monte Carlo methods, In M. Jordan editor,
Learning in graphical models, pp. 175-204, MIT Press, 1999
[11] A. J. Davison, Real-time simultaneous localisation and mapping with
a single camera, IEEE Int. Conf. on Computer Vision, ICCV-2003, pp.
1403-1410, Nice (France), October 2003.
[12] R.T. Collins, Multi-image focus of attention for rapid site model construction,
IEEE Int. Conf. on Computer Vision and Pattern Recognition,
1997, pp. 575-581, San Juan, Puerto Rico, June 1007.
@article{"International Journal of Information, Control and Computer Sciences:64070", author = "Pablo Barrera and Jose M. Canas and Vicente Matellan", title = "Visual Object Tracking in 3D with Color Based Particle Filter", abstract = "This paper addresses the problem of determining the current 3D location of a moving object and robustly tracking it from a sequence of camera images. The approach presented here uses a particle filter and does not perform any explicit triangulation. Only the color of the object to be tracked is required, but not any precisemotion model. The observation model we have developed avoids the color filtering of the entire image. That and the Monte Carlotechniques inside the particle filter provide real time performance.Experiments with two real cameras are presented and lessons learned are commented. The approach scales easily to more than two cameras and new sensor cues.
", keywords = "Monte Carlo sampling, multiple view, particle filters, visual tracking.", volume = "1", number = "4", pages = "1151-4", }
