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 the stride, respectively. A radial basis function (RBF) interpolation framework is used to learn subject specific mapping matrices for mapping from manifold to image space. Given an input image sequence of speed transited gait of a test subject, we estimate the mapping matrix of the test subject as well as the phase and stride sequence using an energy minimization framework considering the following three points: (1) fitness of the synthesized images to the input image sequence as well as to an eigenspace constructed by exemplars of training subjects, (2) smoothness of the phase and the stride sequence, and (3) pitch and stride fitness to the pitch-stride preference model. Using the estimated mapping matrix, we synthesize a constant-speed gait image sequence, and extract a conventional period-based gait feature from it for matching. We conducted experiments using real speed transited gait image sequences with 179 subjects and demonstrated the effectiveness of the proposed method.

Gait is a unique and promising behavioral biometrics which allows to authenticate a person even at a distance from the camera. Since a matching pair of gait features are often drawn from different views due to differences in camera position/attitude and walking directions in the real world, it is important to cope with cross-view gait recognition. In this paper, we propose a discriminative approach to cross-view gait recognition using view-dependent projection matrices, unlike the existing discriminant approaches which utilize only a single common projection matrix for different views. We demonstrated the effectiveness of the proposed method through cross-view gait recognition experiments with two publicly available gait datasets. In addition, since the success of the discriminant analysis relies on the training sample size, we show the effect of transfer learning across two gait datasets as well as provide the rigorous sensitivity analysis of the proposed method against the number of training subjects ranging from 10 to approximately 1,000 subjects.

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 motion. In this paper, we propose a method for action recognition using dynamic features, derived by applying inverse dynamics to a physics-based representation of the human body. The physics-based model is articulated and actuated with muscles and consists of joints with variable stiffness. Dynamic features under consideration include the torques from the knee and hip joints of both legs and, implicitly, gravity, ground reaction forces, and the pose of the remaining body parts. These features are more discriminative than kinematic features, resulting in a low-dimensional representation for human actions, which preserves much of the information of the original high-dimensional pose. This low-dimensional feature achieves good classification performance even with a relatively small training data set in a simple classification framework such as a hidden Markov model. The effectiveness of the proposed method is demonstrated through experiments on the Carnegie Mellon University motion capture data set and Osaka University Kinect action data set with various actions.

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 manifold is learned from multiple high frame-rate sequences in the training stage. In the test stage, an initial phase for each frame of an input low frame-rate image sequence is estimated based on the standard manifold at first, and the manifold reconstruction and the phase estimation are then iterated to generate better high frame-rate images in the energy minimization framework that ensures the fitness to both the input images and the standard manifold. The proposed method is applied to low frame-rate gait recognition and experiments with real data of 100 subjects demonstrate a significant improvement by the proposed method, particularly for quite low frame-rate videos (e.g., 1 fps).

In this paper, we propose a method of action recognition using dynamics features based on physics model. The dynamics features are composed of torques from knee and hip joints of both legs and implicitly include the gravity, ground reaction forces, and the pose of the remaining body parts. These features are more discriminative than the kinematics features, and they result in a low dimensional representation of a human action which preserves much information of the original high dimensional pose. This low dimensional feature allows us to achieve a good classification performance even with a relatively small training data in a simple classification framework such as HMM. The effectiveness of the proposed method is demonstrated through experiments on the CMU motion capture dataset with various actions.

Service robots need to be able to recognize and identify objects located within complex backgrounds. Since no single method may work in every situation, several methods need to be combined. However, there are several cases when autonomous recognition methods fail. We propose an interactive recognition method in these cases. To develop a natural Human Robot Interaction (HRI), it is necessary that the robot should unambiguously perceive the description of an object given by human. This paper reports on our experiment in which we examined the expressions humans use in describing ordinary objects. The results show that humans typically describe objects using one of multiple colors. The color is usually either that of the object background or that of the largest object portion. Based on these results, we describe our development of a robot vision system that can recognize objects when a user adopts simple expressions to describe the objects. This research suggests the importance of connecting ‘symbolic expressions’ with the ‘real world’ in human-robot interaction.