Extraction of Parametric Human Posture Model Using Genetic Algorithm
Date Issued
2004
Date
2004
Author(s)
Hsu, Chung-An
DOI
en-US
Abstract
Recognition of human behavior has become an interesting research topic in computer vision, having a wide range of applications in virtual reality, surveillance systems, user interfaces, human motion analysis, etc. Recognition of human motion mainly involves two steps: posture determination of the initial frame and continuous motion identification of following frames. In order to obtain the human posture parameters of the target, a parameterized artificial human model is necessary to fit the image feature of the target. The existing methods for human motion modeling which required a large number of parameters of human model, it is difficult to obtain accurate estimation results.
We present in this paper an approach to extract human parametric 3-D model for the purpose of estimating human posture. At first, a generic parameterized human model is developed. Then the task is done in two steps. In the first step, human silhouette is extracted from 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. The evolution ability of genetic algorithms can solve large parameter optimization problems. Genetic operations such as natural selection, crossover, and mutation are performed, and individuals in the next generation are generated. After a certain number of repetitions for these processes, the estimated parameter values are obtained from the individual with the best fitness. Experiments using human images show the promising results.
Subjects
姿勢評估
人體行為辨識
基因演算法
human behavior identification
posture determination
genetic algorithm
Type
thesis
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