PSO-Particle Filter-Based Biometric Measurement for Human Tracking

Zhenyuan Xu; Junzo Watada

doi:10.20965/jaciii.2012.p0533

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JACIII Vol.16 No.4 pp. 533-539

doi: 10.20965/jaciii.2012.p0533

(2012)

Paper:

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PSO-Particle Filter-Based Biometric Measurement for Human Tracking

Zhenyuan Xu and Junzo Watada

Graduate School of Information, Production and Systems, Waseda University, 2-7 Hibikino, Wakamatsu-ku, Kitakyushu, Fukuoka 808-0135, Japan

Received:

December 30, 2011

Accepted:

April 15, 2012

Published:

June 20, 2012

Keywords:

height surveying, human tracking, accuracy, particle filter

Abstract

Today, security and surveillance systems are required not only to track the motions of humans but also, in some situations, to recognize and measure biometric features such as width and length. Few methods have been proposed for biometric height measurement in human tracking. Some studies have shown that an infrared ray technique can survey the height of a human, but the equipment required is complicated. The objective of this paper is to build a mathematical model to measure the biometrics of human tracking. This tracking method can show humans’ and objects’ size in a picture so that, if we put this picture in a frame of axes, we can calculate the height and other biometric lengths. To obtain the most accurate results for biometric length surveillance, we need a tracking method that is more exact than conventional tracking results. Combining tracking and detection methods using a particle swarm optimization-particle filter shows results with great accuracy in human tracking.

Cite this article as:

Z. Xu and J. Watada, “PSO-Particle Filter-Based Biometric Measurement for Human Tracking,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.4, pp. 533-539, 2012.

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References

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This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

[1] [1] C. Sacchi and C. S. Regazzoni, “A distributed surveillance system for detection of abandoned objects in unmanned railway environments,” IEEE Trans. on Vehicular Technology, Vol.49, pp. 2013-2026, 2000.

[2] [2] M. Isard and A. Blake, “Contour tracking by stochastic propagation of conditional density,” Computer Vision-ECCV’96, Lecture Notes in Computer Science, Vol.1064, pp. 343-356, 1996.

[3] [3] D. W. Repperger, S. L. Ward, E. J. Hartzell, B. C. Glass, and W. C. Summers, “An Algorithm to Ascertain Critical Regions of Human Tracking Ability,” IEEE Trans. on Systems, Man and Cybernetics, Vol.9, pp. 183-196, 1979.

[4] [4] Z. B. Musa and J. Watada, “Video Tracking System: A survey,” An Int. J. of research and surveys (ICIC express letters), Vol.2, No.1, pp. 65-72, March 2008.

[5] [5] B. Ristic, S. Arulampalam, and N. Gordon, “Beyond the Kalman Filter: Particle Filters for Tracking Applications,” Artech House, 2004.

[6] [6] Z. H. Khan, I. Y. Gu, and A. G. Backhouse, “Robust Visual Object Tracking Using Multi-mode Anisotropic Mean Shift and Particle Filters,” IEEE Trans. on Circuits and Systems for Video Technology, Vol.21, pp. 74-87, 2011.

[7] [7] Y. Zhai, M. B. Yeary, S. Cheng, and N. Kehtarnavaz, “An Object-Tracing Algorithm Based on Multiple-Model Particle Filtering With State Partitioning,” IEEE Trans. on Instrumentation and Measurement, Vol.58, pp. 1797-1809, 2009.

[8] [8] J. MacCormick and A. Blake, “A probabilistic exclusion principle for tracking multiple objects,” Proc. Int. Conf. Comput. Vision, pp. 572-578, 1999.

[9] [9] J. Carpenter, P. Clifford, and P. Fearnhead, “Improved particle filter for nonlinear problems,” IEEE Proc. Radar, Sonar and Navigation, Vol.146, pp. 2-7, 1999.

[10] [10] L. Ye, Q. Zhang, and L. Guan, “Use hierarchical genetic particle filter to figure articulated human tracking,” IEEE Int. Conf. on Multimedia and Expo, pp. 1561-1564, 2008.

[11] [11] D. Crisan, P. Del Moral, and T. J. Lyons, “Discrete Filtering Using Branching and Interacting Particle Systems,” Markov Processes Related Fields, Vol.5, No.3, pp. 293-319, 1999.

[12] [12] S. J. McKenna and H. Nait-Charif, “Tracking human motion using auxiliary particle filters and iterated likelihood weighting,” Image and Vision Computing, Vol.25, Issue 6, pp. 852-862, 2007.

[13] [13] N. J. Gordon, D. J. Salmond, and A. F. M. Smith, “Novel approach to nonlinear/non-Gaussian Bayesian state estimation,” IEEE Proc. F, Vol.140, No.2, pp. 107-113, 1993.

[14] [14] G. Kitagawa, “Monte carlo filter and smoother for non-Gaussian nonlinear state space models,” J. of Computational and Graphical Statistics, Vol.5, No.1, pp. 1-25. 1996.