CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking

by Fakheredine Keyrouz
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 3
Year of Publication: 2012
Authors: Fakheredine Keyrouz
10.5120/9669-4098

Fakheredine Keyrouz . A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking. International Journal of Computer Applications. 60, 3 ( December 2012), 1-6. DOI=10.5120/9669-4098

@article{ 10.5120/9669-4098,
author = { Fakheredine Keyrouz },
title = { A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 3 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 1-6 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number3/9669-4098/ },
doi = { 10.5120/9669-4098 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:36.713562+05:30
%A Fakheredine Keyrouz
%T A Fast-Multiplying PSO Algorithm for Real-Time Multiple Object Tracking
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 3
%P 1-6
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The problem of real-time object tracking in live video sequences is of increasing importance today mainly due to higher security requirements for surveillance applications. In this study we present a novel particle swarm optimization (PSO) algorithm with additional new features. The basic idea of PSO is to use one swarm or one hierarchical swarm of particles to find the best estimate or the global optimum for the object location in a given search space. Particles fly around, share information with each other, and optimize their behavior to find the global optimum. Until today, PSO was used to track one pre-classified pattern of objects. The existing algorithms apply only one swarm of particles to track predefined patterns. The algorithm we present in this paper extended the PSO algorithm to track different objects having non-predefined patterns: n swarms are used to track n objects, i. e. to find n local maxima in different parts of the search space. The proposed algorithm introduces two new components to PSO. A self-adapting component, which is robust against drastic brightness changes of the image sequence, and a self-splitting component, which decides to track the scene as one connected object, or as more stand-alone objects.

References
  1. B. Babenko, M. Yang, and B. Belongie. Robust object tracking with online multiple instance learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 33(8):1619–1632, 2011.
  2. J. Canny. A computational approach for edge detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 8(6):679–698, 1986.
  3. A. Carlisle and G. Dozier. An off-the-shelf PSO. Proc. Workshop on Particle Swarm Optimization, Indianapolis, 2001.
  4. D. Comaniciu and P. Meer. Mean shift: a robust approach toward feature space analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(5):603–619, 2002.
  5. D. Forsyth D. Ramanan and A. Zisserman. Tracking people by learning their appearance. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29(1):65–81, 2007.
  6. X. Zhang et al. Multiple object tracking via species-based particle swarm optimization. IEEE Transactions on Circuits and Systems for Video Technology, 20(11):1590– 1602, 2010.
  7. D. Goldberg. Genetic algorithms in search, optimization and machine learning. Reading, MA: Addison-Wesley, 1989.
  8. J. Kennedy and R. C. Eberhart. Particle swarm optimization. Proc. IEEE International Conference Neural Networks, 4:1942–1948, 1995.
  9. J. Pugh and A. Martinoli. Inspiring and modeling mulitrobot search with particle swarm optimization. In Proc. IEEE Swarm Intelligence Symposium (SIS 2007), pages 332–339, 2007.
  10. M. Scheutz. Real-time hierarchical swarms for rapid adaptive multi-level pattern detection and tracking. In Proc. IEEE Swarm Intelligence Symposium (SIS 2007), pages 234–241, 2007.
  11. G. Welch and G. Bishop. An introduction to Kalman filter. ACM SIGGRAPH 2001 Course #8. , year =.
  12. Z. Zhang, H. Seah, and J. Sun. A hybrid particle swarm optimization with cooperative method for multi-object tracking. Proc. IEEE world Congress on Evolutionary Computation (CEC), 4:1–6, June 2012.
  13. Y. Zheng and Y. Meng. The PSO-based adaptive window for people tracking. In Proc. IEEE Swarm Intelligence Symposium (SIS 2007), pages 23–29, 2007.
Index Terms

Computer Science
Information Sciences

Keywords

Object Tracking Particle Swarm Optimization Real-time performance Self-splitting. ifx

Powered by PhDFocusTM