Human Head Tracking Based on Particle Swarm Optimization and Genetic Algorithm
Indra Adji Sulistijono*,** and Naoyuki Kubota***,****
*Dept. of Mechanical Engineering, Graduate School of Engineering, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
**Electronics Eng. Polytechnic Institute of Surabaya - ITS (EEPIS-ITS), Kampus ITS Sukolilo, Surabaya 60111, Indonesia
***Dept. of System Design, Tokyo Metropolitan University, 6-6 Asahigaoka, Hino, Tokyo 191-0065, Japan
****SORST, Japan Science and Technology Agency (JST)
This paper compares particle swarm optimization and a genetic algorithm for perception by a partner robot. The robot requires visual perception to interact with human beings. It should basically extract moving objects using visual perception in interaction with human beings. To reduce computational cost and time consumption, we used differential extraction. We propose human head tracking for a partner robot using particle swarm optimization and a genetic algorithm. Experiments involving two maximum iteration numbers show that particle swarm optimization is more effective in solving this problem than genetic algorithm.
-  R. A. Brooks, “Cambrian Intelligence,” The MIT Press, 1999.
-  T. Fukuda and N. Kubota, “An Intelligent Robotic System Based on a Fuzzy Approach,” Proc. of IEEE, Vol.87, No.9, pp. 1448-1470, 1999 (Invited Paper).
-  M. T. Turvey and R. E. Shaw, “Ecological Foundations of Cognition I. Symmetry and Specificity of Animal-Environment Systems,” Journal of Consciousness Studies 6, No.11-12, pp. 95-110, 1999.
-  M. T. Turvey and R. E. Shaw, “Ecological Foundations of Cognition II. Degree of Freedom and Conserved Quantities in Animal-Environment Systems,” Journal of Consciousness Studies 6, No.11-12, pp. 111-123, 1999.
-  J. Gibson, “The Ecological Approach to Visual Perception,” LEA, 1986.
-  M. Eysenck, “Perception and Attention,” Michael Eysenck (Eds.), Psychology, Prentice Hall, pp. 139-166, 1998.
-  D. Marr, “Vision,” W.H.Freeman, San Francisco, 1982.
-  S. J. Russell and P. Norvig, “Artificial Intelligence,” Prentice-Hall, Inc., 1995.
-  J. S. Jang, C. T. Sun, and E. Mizutani, “Neuro-Fuzzy and Soft Computing,” Prentice-Hall, Inc., 1997.
-  T. Hastie, R. Tibshirani, and J. Friedman, “The Elements of Statistical Learning,” Springer-Verlag, 2001.
-  R. C. Eberhart, Y. Shi, and J. Kennedy, “Swarm Intelligence,” Morgan Kaufmann Publisher, San Francisco, March, 2001.
-  E. Bonabeau, M. Dorigo, and G. Theraulaz, “Swarm Intelligence: From Natural to Artificial Systems,” Oxford University Press, New York, Sept., 1999.
-  G. Syswerda, “A Study of Reproduction on Generational and Steady-State Genetic Algorithms,” Book: Foundations of Genetic Algorithms, Morgan Kaufmann Publishers, San Mateo, pp. 94-101, 1991.
-  Z. Michalewicz, “Genetic Algorithms + Data Structures = Evolution Programs,” Springer, 1996.
-  N. Kubota, D. Hisajima, F. Kojima, and T. Fukuda, “Fuzzy and Neural Computing for Communication of A Partner Robot,” Journal of Multiple Valued Logic and Soft-Computing, Vol.9, No.2, pp. 221-239, 2003.
-  S. Birchfield, “Elliptical Head Tracking Using Intensity Gradients and Color Histograms,” Proc. of the IEEE Conference on Computer Vision and Pattern Recognition, Santa Barbara, California, pp. 232-237, 1998.
-  M. Shanahan and D. Randell, “A Logic-Based Formulation of Active Visual Perception,” Journal American Association for Artificial Intelligence, 2004.
-  A. Bissacco and S. Soatto, “Visual Tracking of Human Body with Deforming Motion and Shape Average,” UCLA CSD Technical Report 020046, November, 2002.
-  Y. Shi, “Particle Swarm Optimization,” Article on IEEE Neural Network Society Bulletin, pp. 8-13, Feb., 2004.
-  F. van den Bergh and A. P. Engelbrecht, “A Cooperative Approach to Particle Swarm Optimization,” Journal of IEEE Trans. on Evolutionary Computation, Vol.8, No.3, pp. 225-239, 2004.
-  M. G. Omran, A. P. Engelbrecht, and A. Salman, “A Color Image Quantization Algorithm Based on Particle Swarm Optimization,” Journal of Informatica, Vol.29, No.3, pp. 261-269, 2005.
-  E. Elbeltagi, T. Hegazy, and D. Grierson, “Comparison among five evolutionary-based optimization algorithms,” Proc. of Advanced Engineering Informatics 29, pp. 43-53, 2005.
-  N. Kubota, I. A. Sulistijono, and Y. Ito, “Visual Perception for A Partner Robot Based on Computational Intelligence,” Proc. of the International Symposium on Computational Intelligence and Industrial Applications (ISCIIA2004), Haikou-Hainan China, 2004.
-  N. Kubota and K. Nishida, “Human Recognition of A Partner Robot Based on Relevance Theory and Neuro-Fuzzy Computing,” Proc. of International Conference on Intelligent Robots and Systems (IROS2005), pp. 2812-2817, 2005.
-  I. A. Sulistijono and N. Kubota, “Human Clustering For A Partner Robot Based on Computational Intelligence,” Proc. of the Fuzzy System and Knowledge Discovery (FSKD05) LNAI-3613, Springer, pp. 1001-1010, Sep., 2005.
-  I. A. Sulistijono and N. Kubota, “Visual Perception For A Partner Robot Based on Computational Intelligence,” Journal of Advanced Computational Intelligence and Intelligent Informatics (JACIII), Vol.9, No.6, pp. 654-660, 2005.
-  I. A. Sulistijono and N. Kubota, “Human Clustering for A Partner Robot Based on Particle Swarm Optimization,” Proceeding of The 15th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN 06), Hatfield, United Kingdom, pp. 686-691, 2006.
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