Two-layer dual gait generative models for human motion estimation from a single camera

This paper presents a two-layer gait representation framework for video-based human motion estimation that extends our recent dual gait generative models, visual gait generative model (VGGM) and kinematic gait generative model (KGGM), with a new capability of part-whole gait modeling. Specifically, the idea of gait manifold learning is revisited to capture the gait variability among different individuals at both whole and part levels. A key issue is the selection of an appropriate distance metric to evaluate the dissimilarity between two gaits (either at whole or part levels) that determines an optimal manifold topology. Several metrics are studied and compared in terms of their effectiveness for gait manifold learning at both whole and part levels. This work involves one whole-based and two part-level gait manifolds by which three pairs of KGGM and VGGM can be learned and integrated for part-whole gait modeling. Moreover, a two-stage Monte Carlo Markov Chain (MCMC) inference algorithm is developed for video-based part-whole motion estimation. The proposed algorithm is tested on the HumanEva data and reaches state-of-art results.

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► Two-layer whole-part generative models allow more accurate modeling of human gaits.
► Gait manifolds can be learned with different distance metrics for general gait modeling.
► A Fourier series-based distance metric is effective for gait manifold learning.
► The two-layer gait modeling improves the performance of video-based motion estimation.