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In this paper, we propose a temporal super resolution approach for quasi-periodic image sequence such as human gait. The proposed method effectively combines example-based and reconstruction-based temporal super resolution approaches. A periodic image sequence is expressed as a manifold parameterized by a phase and a standard mani-fold is learned from(More)
Pose-based approaches for human action recognition are attractive owing to their accurate use of human motion information. Traditionally, such approaches used kinematic features for classification. However, in addition to having high dimensions and a small interclass variation, kinematic features do not consider the interaction of the environment on human(More)
Service robots need object recognition strategy that can work on various objects in complex backgrounds. Since no single method can work in every situation, we need to combine several methods so that the robots can use the appropriate one automatically. In this paper we propose a scheme to classify situations depending on the characteristics of object of(More)
This paper describes a method of gait recognition from image sequences wherein a subject is accelerating or decelerating. As a speed change occurs due to a change of pitch (the first-order derivative of a phase, namely, a gait stance) and/or stride, we model this speed change using a cylindrical manifold whose azimuth and height corresponds to the phase and(More)
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(More)