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This paper proposes a novel computer vision approach applied to video sequences of people walking to recognize them by their gait. Human motion carries different information considering different ways of analysis. The skeleton carries motion information about human joints and the silhouette carries information about boundary motion of the human body. The binary and gray-level-images contain different information about the human motion. Our proposal is to recover these different kinds of information of the human body based on four different segmented image models, and apllies a fusion model to improve classification. The basic idea is to consider frames as elements of distinct classes of video sequences and the sequences themselves as classes in a database. The methodology applies background extraction using the Gaussian Mixture Model (GMM), a scale reduction using a multiresolution technique based on the Wavelet Transform (WT) and feature extraction using Principal Component Analysis (PCA). The Classification rates obtained separately from four different kinds of image sequences are them merged together by a fusion technique.

Método de reconhecimento da marcha humana por meio da fusão das características do movimento global