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| A Hierarchical Algorithm for Human Posture Recognition Based on Spatial and Frequency Domain Features |
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Abstract Human posture recognition is a research hotspot in the automatic video understating technology. It is difficulty to ensure the accuracy, robustness and realtime at the same time in practical applications. The existing mainstream algorithms based on 2D image information can be classified into two classes: the methods based on high level human model which have high accuracy and highcomplexity; the methods based on low level image information which have low complexity and low accuracy. A algorithm for human posture recognition is proposed to solve this problem. Firstly, Gaussian mixture model is exploited to extract foreground and normalized human silhouette. Then, a 12 dimensional invariant eigenvector is constructed, thereby the human posture model is established. Finally, a hierarchical recognition method is adopted to recognize the postures. This algorithm is efficient, has low complexity, and achieves good effect for some interfered images. The results of the experiments based on standard video database verify the validity of the proposed algorithm, and the superiority of the algorithm is also verified compared with chain code algorithm.
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2011, Vol. 24 
