Abstract
A gait feature extraction method based on resampling shape context is proposed in this article. First, the moving target detection is carried out to obtain the target area of the human body. Second, the gait cycle is measured, and the contour points and the lower limb joints are selected as sampling points. Then, the different sampling points is placed in the polar coordinates of the origin, the number of sampling points in different cells is counted as the shape context; Finally, the feature vectors are constructed according to the shape context, and the minimum distance is used for classification and recognition. Simulation experiments based on resampling shape context are tested in CASIA gait Database A and Database B. The experimental results show that the method proposed in this article has a lower computational complexity and higher recognition rate when compared with the original shape context method, which can be used for gait recognition.
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