Top > JACIII > Tracking of Multiple Moving Objects in Dynamic Image of Omni- ...

single-jc.php

JACIII Vol.12 No.1 pp. 16-25
doi: 10.20965/jaciii.2008.p0016
(2008)

Paper:

Views over last 60 days: 428

Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter

Norikazu Ikoma*, Ryuichi Yamaguchi**, Hideaki Kawano*,
and Hiroshi Maeda*

*Faculty of Engineering, Kyushu Institute of Technology, 1-1 Sensui-cho, Tobata-ku, Kita-Kyushu, Fukuoka 804-8550, Japan

**NTT DATA Corporation, Toyosu Center Building, 3-3-3 Toyosu, Koto-ku, Tokyo 135-6033, Japan

Received:
March 30, 2007
Accepted:
August 30, 2007
Published:
January 20, 2008
Creative Commons License
Keywords:
visual tracking, sequential Monte Carlo (SMC), probability hypothesis density (PHD) filter, finite random set, omni-directional camera
Abstract
A method of multiple moving objects tracking in dynamic image of omni-directional camera has been proposed. Finite random set (FRS) based state space model is employed in the method due to its inherent nature capable to represent the scene having occlusion and appearance of object as well as missing and false detection in observation. Sequential Monte Carlo (SMC) implementation of Probability hypothesis density (PHD) filter has been used for estimating state of the state space model. The state is a finite random set of single object states, where each element of the set consists of position and velocity of the object in panoramic image coordinate of omni-directional camera image. We propose a new method to display tracking result from weighted particles obtained from the estimation process by SMC implementation of PHD filter. Key idea of the method is to put an integer label on each particle, where the label indicates specific object among multiple objects in the image scene tracked by the particle. Numerical simulation and real image experiments illustrate tracking performance of the proposed method.
Cite this article as:
N. Ikoma, R. Yamaguchi, H. Kawano, and H. Maeda, “Tracking of Multiple Moving Objects in Dynamic Image of Omni-Directional Camera Using PHD Filter,” J. Adv. Comput. Intell. Intell. Inform., Vol.12 No.1, pp. 16-25, 2008.
Data files:
References
  1. [1] A. Doucet, N. de Freitas, and N. J. Gordon (eds.), “Sequential Monte Carlo Methods in Practice,” New York, Springer, 2001.
  2. [2] I. R. Goodman, R. Mahler, and H. T. Nguyen, “Mathematics of data fusion,” Kluwer Academic Publishers, pp. 131-217, 1997.
  3. [3] B.-N. Vo, S. Singh, and A. Doucet, “Sequential Monte Carlo methods for Multi-target Filtering with Random Finite Sets,” IEEE Trans. on Aerospace and Electronic Systems, Vol.41, Issue 4, pp. 1224-1245, 2005.
  4. [4] R. Mahler, “A theoretical foundation for the Stein-Winter Probability Hypothesis Density (PHD) multitarget tracking approach,” Proc. 2002 Military Sensing Symposia, National Symposium on Sensor and Data Fusion, 1, 2002.
  5. [5] N. Ikoma, T. Uchino, and H. Maeda, “Image motion tracking by FRS state space model using SMC implementation of PHD filter,” IEEE Visual Communications and Image Processing (VCIP) 2005, pp.129-140, Beijing, China, July 12-15, 2005.
  6. [6] T. Uchino, N. Ikoma, and H. Maeda, “Tracking of feature points in dynamic image with occlusion and appearance by SMC implementation of PHD filter,” Joint 2nd Int. Conf. on Soft Computing and Intelligent Systems and 5th Int. Symposium on Advanced Intelligent Systems, (SCIS & ISIS 2004), Sep. 21-24, Yokohama, Japan, CD-ROM, paper #THP-4-4, 2004.
  7. [7] N. Ikoma, T. Uchino, and H. Maeda, “Tracking of Feature Points in Image Sequence by SMC Implementation of PHD Filter,” Proc. of SICE Annual Conf. 2004, pp. 1696-1701, Sapporo, Japan, 2004.
  8. [8] N. Ikoma, R. Yamaguchi, H. Kawano, and H. Maeda, “Tracking of Multiple Moving Objects in Dynamic Image of Omni-directional Camera for Robot Vision Using PHD Filter,” 2nd Int. Symposium on Computational Intelligence and Industrial Applications (ISCIIA2006), pp. 168-176, 2006.

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

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 29, 2024