Abstract
We investigate a robust sequential estimation algorithm of particle filters, which combine multiple features of visual objects, in order to obtain reliable evidential information from independent sources of sensor data. Most of particle filter algorithms are based on conditional density propagation in Bayesian inference rules. In this paper, it is modified by the conjunctive rule of independent features. Therefore, the proposed algorithm is more reliable since it demonstrates the solution to both efficiency depletion and over-sampling in particle filters.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
J. M. Hammersley and K. W. Morton, “Poor man’s monte carlo,” J. of the Royal Statistical Society B, vol. 16, pp. 23–38, 1954.
N. Gordon, D. Salmond, and A. Smith, “Novel approach to nonlinear/non-gaussian bayesian state estimation,” IEEE Proc. F, vol. 140, no. 2, pp. 107–113, 1993. [click]
M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” Proc. of 4th European Conf. on Computer Vision (ECCV), pp. 343–356, April 1996.
M. Isard and A. Blake, “Condensation-conditional density propagation for visual tracking,” Int. J. Computer Vision, vol. 29, no. 1, pp. 5–28, 1998. [click]
J. Carpenter, P. Clifford, and P. Fearnhead, “An improved particle filter for non-linear problems,” IEE proceedings Radar, Sonar and Navigation, vol. 146, pp. 2–7, 1999. [click]
P. Del Moral, A. Doucet, and A. Jasra, “On adaptive resampling procedures for sequential Monte Carlo methods,” Technical Report. INRIA, 2008.
O. Cappé, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential Monte Carlo,” IEEE Proceedings, vol. 95, no. 5, pp. 899–924, 2007. [click]
A. Doucet, S. J. Godsill, and C. Andrieu, “On sequential Monte Carlo sampling methods for Bayesian filtering,” Statistics and Computing, vol. 10, pp. 197–208, 2000. [click]
A. Blake and M. Isard, Active Contours, SpringerVerlag, London, 1998.
A. Popoulis, Probability and Statistics, Prentice Hall, Inc., New York, 1990.
M. Isard and A. Blake, “Icondensation: Unifying low-level and high-level tracking in a stochastic framework,” Proc. of 5th European Conf. on Computer Vision (ECCV), pp.893–908, April 1998. [click]
A. Blake, M. Isard, and D. Reynard, “Learning to track the visual motion of contours,” J. Artificial Intelligence, vol. 78, pp. 101–134, 1995.
S. J. Julier and J. K. Uhlmann, “A new extension of kalman filter to linear system,” Proc. of Aerosense: 11th Int. Symp. on Aerospace/Defense Sensing, Simulation, and Control, Orlando, FL, pp. 182–193, 1997.
J. MacCormick and A. Blake, “Partitioned sampling, articulated objects and interface-quality hand tracking,” Proc. of 7th European Conf. on Computer Vision (ECCV), April 2000.
D. Freedman and T. Zhang, “Active contours for tracking distributions,” IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 518–526, 2004. [click]
F. L. Lewis, Optimal Estimation: with an Introduction to Stochastic Control Theory, John Wiley & Sons, Inc., New York, 1986.
A. Dempster, “Upper and lower probabilities induced by a multivalued map**,” Annals of Mathematical Statistics, vol. 38, pp. 325–339, 1967.
G. Shafer, A Mathematical Theory of Evidence, Princeton University Press, New Jersey, 1976.
C. Gentile, O. Camps, and M. Sznaier, “Segmentation for robust tracking in the presence of severe occlusion,” IEEE Transactions on Image Processing, vol. 13, no. 2, pp. 166–178, 2004.
F. Gustafsson, F. Gunnarsson, N. Bergman, U. Forssell, J. Jansson, R. Karlsson, and P.-J. Nordlund, “Particle filters for positioning, navigation, and tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 425–437, Feb. 2002. [click]
C. Hue, J.-P. Le Cadre, and P. Perez, “Tracking multiple objects with particle filtering,” IEEE Transactions on Aerospace and Electronic Systems, vol. 38, no. 3, pp.791–812, 2002. [click]
Dubuisson and Sverine, Tracking with Particle Filter for High-dimensional Observation and State Spaces, JohnWiley & Sons Inc., 2014.
H. Bi, J. Ma, and F. Wang, “An improved particle filter algorithm based on ensemble Kalman filter and Markov chain Monte Carlo cethod,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, no. 2, pp. 447–459, Feb. 2015. [click]
Author information
Authors and Affiliations
Corresponding author
Additional information
Recommended by Associate Editor Young Soo Suh under the direction of Editor PooGyeon Park. This research was supported by the Chung-Ang University Graduate Research Scholarship in 2015, and by the national research foundation of Korea with the grant no. NRF-2017R1D1A1B03036450. The authors would like to thank those who spare their invaluable time and effort for supporting or reviewing our academic research work.
Hoon Kang was born in Seoul, Korea, in 1959. He received the B.S. and M.S. degrees in electronic engineering from the Seoul National University, Korea, in 1982 and 1984, respectively. He earned the Ph.D. degree and the CIMS certificate in the School of Electrical Engineering at the Georgia Institute of Technology, Atlanta, in 1989. From 1989 to 1991, he was first a postdoctoral fellow and then a research associate in the Georgia Tech Electrical Engineering Department. As he participated in a number of projects sponsored by the national science foundation, the office of naval research, the ford motor company, and honeywell inc., he developed new research ideas on fuzzy logic control, intelligent robotic control, and fault detection and identification. He also joined a NASA SBIR project on active dam** control of the remote manipulator systems when he worked for automation concepts and systems, inc., as a research engineer in 1991. Since 1992, He joined the School of Electrical and Electronics Engineering at Chung-Ang University, Seoul, Korea. He has served as the deparment chair, the financial secretary both for the Korean Intelligent Information Systems (KIIS) and for the Institute of Control, Robotics and Systems Engineers (ICROS). He also joined the Institute of Electronics Engineers of Korea (IEEK) as a financial secretary, an editorial board member, and a general affairs director. His research interests include computational intelligence such as fuzzy systems, neural networks, evolutionary computation, artificial life; robotics and robot vision such as visual tracking, object recognition, human computer interfaces, intelligent robots, and humanoids.
Hyun Su Lee received his B.S. degree from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2014. He is currently working toward an M.S. degree in intelligent robot at Chung-Ang University. Now, he joined Intelligent robot and vision lab, working on image detection and neural networks. His research interests are artificial intelligence, intelligent control, robot vision, and image processing.
Young-Bin Kwon joined to Chung-Ang University, Seoul, Korea as a Professor since March 1986 just after finishing Ph.D. degree from ENST, Paris, France. He was a Governing Board member of IAPR from 1995 to 2010. He served as the chairman of TC10 of IAPR from 2000 to 2002. He was elected as a convenor of WG2 on biometric technical interface on ISO/IEC JTC1/SC37 in 2002. He also serves a liaison officer between SC37 from/to SC31 (AIDC). He is a chairman of SC31 Korea and SC37 Korea. He is the director of Electronic and Information Reserach Information Center funded by National Research Foundation since 1999. His research is pattern recognition, graphics recognition, and biometrics and related processing algorithm development.
Ye Hwan Park received his B.S. degree from the School of Electrical and Electronics Engineering, Chung-Ang University, Seoul, Korea, in 2014. He is currently working toward an M.S. degree in intelligent robot at Chung-Ang University. Now, he joined Intelligent robot and vision lab, working on object detection and recognition using deep learning and intelligent robot control. His research interests include robotics, image processing, deep learning and other neural networks.
Rights and permissions
About this article
Cite this article
Kang, H., Lee, H.S., Kwon, YB. et al. A Sequential Estimation Algorithm of Particle Filters by Combination of Multiple Independent Features in Evidence. Int. J. Control Autom. Syst. 16, 1263–1270 (2018). https://doi.org/10.1007/s12555-016-0644-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12555-016-0644-z