Jian Luo1,*, Bo Xu1, Tardi Tjahjadi2, Jian Yi1
CMC-Computers, Materials & Continua, Vol.80, No.1, pp. 235-261, 2024, DOI:10.32604/cmc.2024.050018
Abstract Subject identification via the subject’s gait is challenging due to variations in the subject’s carrying and dressing conditions in real-life scenes. This paper proposes a novel targeted 3-dimensional (3D) gait model (3DGait) represented by a set of interpretable 3DGait descriptors based on a 3D parametric body model. The 3DGait descriptors are utilised as invariant gait features in the 3DGait recognition method to address object carrying and dressing. The 3DGait recognition method involves 2-dimensional (2D) to 3DGait data learning based on 3D virtual samples, a semantic gait parameter estimation Long Short Time Memory (LSTM) network (3D-SGPE-LSTM), a feature fusion… More >
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