Hierarchical temporal and spatial memory for gait pattern recognition

Jianghao Shen, Murray H. Loew

Abstract: This research extends the Hierarchical Temporal Memory (HTM) algorithm and applies it to gait recognition. The gait sequence first is decomposed into temporal sub-sequences of spatial sub-regions. The sub-sequence are defined as the period of one step and half step, and the sub-regions are defined as the areas that correspond to body parts. Each sub-area will learn the temporal variation of the body part by constructing Markov Chains. Finally, the classification result is the concatenation of the beliefs of all sub-areas. Unlike other methods, which use gait-specific features, our method uses only image patches of sub-areas. Our extension of previous versions of HTM provides hierarchical temporal inference to cumulate the belief. This generalized new approach is evaluated on a dataset of 151 subjects and two walking conditions. It compares favorably to other current methods used with those data, without requiring problem-specific inputs.