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Modeling timing structures in gait image sequences using bottom-up clustering

In this paper, we show that timing differences among body parts characterize human gait motions. To represent the timing differences, the timing structures are introduced and are derived using bottom-up clustering. In our method, multiple cameras capture image sequences to analyze human gait motions. A 3-D human articulated model is fitted to the reconstructed 3-D shape of a walking person. The 3-D articulated model is tracked by evaluating the space between the 3-D model and the reconstructed 3-D shape. Feature vectors of selected individual body parts are segmented by performing bottom-up clustering on linear dynamical systems. The timing structures of a walking person are described. Experimental results show that the proposed method is effective for timing difference analysis, and the timing structures can represent the individuality.