## Stochastic Tracking of 3D Human Figures Using 2D Image Motion (2000)

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Venue: | In European Conference on Computer Vision |

Citations: | 316 - 33 self |

### BibTeX

@INPROCEEDINGS{Sidenbladh00stochastictracking,

author = {Hedvig Sidenbladh and Michael J. Black and D. J. Fleet},

title = {Stochastic Tracking of 3D Human Figures Using 2D Image Motion},

booktitle = {In European Conference on Computer Vision},

year = {2000},

pages = {702--718}

}

### Years of Citing Articles

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### Abstract

. 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 dierences, and a prior probability distribution over pose and joint angles that models how humans move. The posterior probability distribution over model parameters is represented using a discrete set of samples and is propagated over time using particle ltering. The approach extends previous work on parameterized optical ow estimation to exploit a complex 3D articulated motion model. It also extends previous work on human motion tracking by including a perspective camera model, by modeling limb self occlusion, and by recovering 3D motion from a monocular sequence. The explicit posterior probability distribution represents ambiguities due to image matching, model singularities, and perspective projection. The method relies only on a...

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Citation Context ...generality, we assume that each limb is textured mapped with an appearance model, R. There are many ways in which one might specify such a model, including the use of low-dimensional linear subspaces =-=[20]-=-. Moreover, it is desirable, in general, to estimate the appearance parameters through time to reflect the changing appearance of the object in the image. Here we use a particularly simple approach in... |

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Citation Context ...The approach here focuses on the estimation of 3D articulated motion from 2D image changes. In so doing we exploit recent work on the probabilistic estimation of optical flow using particle filtering =-=[1,2]-=-. The method has been applieds704 H. Sidenbladh, M.J. Black, and D.J. Fleet to non-linear spatial and temporal models of optical flow, and is extended here to model the motion of articulated 3D object... |

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Citation Context ...l models of body limb or joint motion also vary in complexity; they include smooth motion [7], linear dynamical models [18], nonlinear models learned from training data using dimensionality reduction =-=[3,16,23]-=-, and probabilistic Hidden Markov Models (HMM’s) (e.g., [4]). In many of these methods, image measurements are first computed and then the temporal models are applied to either smooth or interpret the... |

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Citation Context ...l models of body limb or joint motion also vary in complexity; they include smooth motion [7], linear dynamical models [18], nonlinear models learned from training data using dimensionality reduction =-=[3,16,23]-=-, and probabilistic Hidden Markov Models (HMM’s) (e.g., [4]). In many of these methods, image measurements are first computed and then the temporal models are applied to either smooth or interpret the... |

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Citation Context ...0 3 The approach here focuses on the estimation of 3D articulated motion from 2D image changes. In so doing we exploit recent work on the probabilistic estimation of opticalsow using particlesltering =-=[1, 2]-=-. The method has been applied to non-linear spatial and temporal models of opticalsow, and is extended here to model the motion of articulated 3D objects. Body and Camera Models. Models of the human b... |

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A multiple hypothesis approach to tracking. CVPR
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Citation Context ...rchival movie footage and inexpensive video surveillance equipment. The use of perspective projection allows the model to handle signicant changes in depth. Finally, unlike template tracking methods [=-=6]-=-, the use of image motion allows tracking under changing viewpoint. These properties are illustrated with examples that include tracking people walking in cluttered images while their depth and orient... |