Authors:
Shuhan Shen
and
Weirong Chen
Affiliation:
School of Electrical Engineering, Southwest Jiaotong University, China
Keyword(s):
Tracking, Human Tracking, Probabilistic evolutionary algorithm.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Computer Vision, Visualization and Computer Graphics
;
Human-Computer Interaction
;
Methodologies and Methods
;
Model-Based Object Tracking in Image Sequences
;
Motion and Tracking
;
Motion, Tracking and Stereo Vision
;
Pattern Recognition
;
Physiological Computing Systems
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. The individual in PEA is encoded by the probabilistic compound bit, defined as the smallest unit of information, for the probabilistic representation. The observation step is used in PEA to obtain the observed states of the individual, and the update operator is used to evolve the individual. 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.
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