Extraction of Parametric Human Model for Posture Recognition Using Genetic Algorithm

Authors:
Changbo Hu;Qingfeng Yu;Yi Li;Songde Ma
Affiliations:
-;-;-;-
Venue:
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
Year:
2000

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Abstract

We present in this paper an approach to extract human parametric 2-D model for the purpose of estimating human posture and recognizing human activity. This task is done in two steps. In the first step, human silhouette is extracted from complex background under a fixed camera through a statistical method. By this method, we can reconstruct the background dynamically and obtain the moving silhouette. In the second step, genetic algorithm is used to match the silhouette of human body to a model in parametric shape space. In order to reduce the searching dimension, a layer method is proposed to take the advantage of human model. Additionally we apply structure-oriented Kalman filter to estimate the motion of body parts. Therefore initial population and value in GA can be well constrained. Experiments on real video sequences show that our method can extract human model robustly and accurately.