Probability evolutionary algorithm based human body tracking

Authors:
Shuhan Shen;Weirong Chen
Affiliations:
School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China;School of Electrical Engineering, Southwest Jiaotong University, Chengdu, China
Venue:
EuroGP'06 Proceedings of the 2006 international conference on Applications of Evolutionary Computing
Year:
2006

Citing 6
Cited 1

CONDENSATION—Conditional Density Propagation forVisual Tracking

International Journal of Computer Vision
3-D model-based tracking of humans in action: a multi-view approach

CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Tracking Articulated Body by Dynamic Markov Network

ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Tracking multiple humans in crowded environment

CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A survey on visual surveillance of object motion and behaviors

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization

IEEE Transactions on Evolutionary Computation

A unified direct approach to image registration and object recognition with a hybrid evolutionary algorithm

ICIC'09 Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

A novel evolutionary algorithm called Probability Evolutionary Algorithm (PEA), and a method based on PEA for visual tracking of human body are presented. PEA is inspired by the Quantum computation and the Quantum-inspired Evolutionary Algorithm, and it has a good balance between exploration and exploitation with very fast computation speed. In the PEA based human tracking framework, tracking is considered to be a function optimization problem, so the aim is to optimize the matching function between the model and the image observation. Then PEA is used to optimize the matching function. Experiments on synthetic and real image sequences of human motion demonstrate the effectiveness, significance and computation efficiency of the proposed human tracking method.