In this paper, we introduce a torus manifold-based temporal super resolution method for gait recognition from low frame-rate videos with view transitions. Given a low frame-rate gait sequence with view transition from an unknown person, we estimate three unknowns: view, phase, and style. We estimate view by walking trajectory and camera information, phase by dynamic programming using multiview exemplar sequences, and style by bilinear model and linear least squares. Once these parameters are known, we can synthesize a high frame-rate sequence corresponding to that unknown person and can use existing methods for gait recognition. Experiments with OU-ISIR multiview gait dataset demonstrate the effectiveness of the proposed method for frame-rates as low as 1 or 2 fps.
['Al Mansur', 'Yasushi Makihara', 'Yasushi Yagi']
View-invariant gait recognition from low frame-rate videos