Evolutionary robot vision and particle swarm optimization for Multiple human heads tracking of a partner robot

This paper discusses the advantage and disadvantage of evolutionary robot vision and particle swarm optimization for multiple human heads tracking. Evolutionary robot vision combines the technologies of the evolutionary computation and robot vision. Both of evolutionary computation and particle swarm optimization can perform the multiple human heads tracking well for feasible solution in a dynamic movement. This paper compares their performance. Finally, the proposed method is applied to a partner robot, and we discuss the effectiveness of the multiple human heads tracking in the natural communication with humans.

[1]  Murray Shanahan,et al.  A Logic-Based Formulation of Active Visual Perception , 2004, KR.

[2]  Marco Dorigo,et al.  Swarm intelligence: from natural to artificial systems , 1999 .

[3]  Trevor Hastie,et al.  The Elements of Statistical Learning , 2001 .

[4]  Michael T. Turvey,et al.  Ecological foundations of cognition. II: Degrees of freedom and conserved quantities in animal-environment systems , 1999 .

[5]  Naoyuki Kubota,et al.  Human Clustering for A Partner Robot Based on Particle Swarm Optimization , 2006, ROMAN 2006 - The 15th IEEE International Symposium on Robot and Human Interactive Communication.

[6]  P. A. Adams,et al.  Perception and attention. , 1958, The American journal of psychology.

[7]  Naoyuki Kubota,et al.  A Comparison of Particle Swarm Optimization and Genetic Algorithm for Human Head Tracking , 2006 .

[8]  M. Turvey,et al.  Ecological foundations of cognition. I: Symmetry and specificity of animal-environment systems , 1999 .

[9]  Fumio Kojima,et al.  Fuzzy and Neural Computing for Communication of a Partner Robot , 2003, J. Multiple Valued Log. Soft Comput..

[10]  Ross Francis Walker.  Adaptive multi-scale texture analysis : with application to automated cytology , 1997 .

[11]  Andries Petrus Engelbrecht,et al.  A Cooperative approach to particle swarm optimization , 2004, IEEE Transactions on Evolutionary Computation.

[12]  Naoyuki Kubota,et al.  Human Clustering for a Partner Robot Based on Computational Intelligence , 2005, FSKD.

[13]  Andries Petrus Engelbrecht,et al.  A Color Image Quantization Algorithm Based on Particle Swarm Optimization , 2005, Informatica.

[14]  Toshio Fukuda,et al.  An intelligent robotic system based on a fuzzy approach , 1999, Proc. IEEE.

[15]  Naoyuki Kubota,et al.  Visual perception for a partner robot based on computational intelligence , 2004, 2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542).

[16]  Gilbert Syswerda,et al.  A Study of Reproduction in Generational and Steady State Genetic Algorithms , 1990, FOGA.

[17]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1992, Artificial Intelligence.

[18]  J. Gibson.  The Ecological Approach to Visual Perception , 1979 .

[19]  E. Reed.  The Ecological Approach to Visual Perception , 1989 .

[20]  M. Turvey.  Ecological foundations of cognition: Invariants of perception and action. , 1992 .

[21]  Naoyuki Kubota,et al.  Human recognition of a partner robot based on relevance theory and neuro-fuzzy computing , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[22]  Donald E. Grierson,et al.  Comparison among five evolutionary-based optimization algorithms , 2005, Adv. Eng. Informatics.

[23]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[24]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[25]  A. Bissacco.  Visual Tracking of Human Body with Deforming Motion and Shape Average , 2005 .