Hedvig Kjellström

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A probabilistic method for tracking 3D articulated human gures in monocular image sequences is presented. Within a Bayesian framework, we de ne a generative model of image appearance, a robust likelihood function based on image graylevel di erences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior(More)
This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical(More)
The visual analysis of human manipulation actions is of interest for e.g. human-robot interaction applications where a robot learns how to perform a task by watching a human. In this paper, a method for classifying manipulation actions in the context of the objects manipulated, and classifying objects in the context of the actions used to manipulate them is(More)
This paper investigates object categorization according to function, i.e., learning the affordances of objects from human demonstration. Object affordances (functionality) are inferred from observations of humans using the objects in different types of actions. The intended application is learning from demonstration, in which a robot learns to employ(More)
This paper presents a method for detection of humans in video sequences. The intended application of the method is outdoor surveillance. In such an uncontrolled environment, the appearance of humans varies hugely due to clothing, identity, weather and amount and direction of light. The idea is therefore to detect patterns of human motion, which to a large(More)
This study has been performed in order to test the human-machine interface of a computer-based speech training aid named ARTUR with the main feature that it can give suggestions on how to improve articulation. Two user groups were involved: three children aged 9-14 with extensive experience of speech training, and three children aged 6. All children had(More)
We present methods for learning and tracking human motion in video We estimate a statistical model of typical activities from a large set of D periodic human motion data by segmenting these data automatically into cycles Then the mean and the princi pal components of the cycles are computed using a new algorithm that accounts for missing information and(More)
While the problem of tracking 3D human motion has been widely studied, most approaches have assumed that the person is isolated and not interacting with the environment. Environmental constraints, however, can greatly constrain and simplify the tracking problem. The most studied constraints involve gravity and contact with the ground plane. We go further to(More)