Condensation Algorithm
A Probabilistic Framework for Matching Temporal Trajectories: Condensation-Based Recognition of Gestures and Expressions
Abstract: . The recognition of human gestures and facial expressions in image sequences is an important and challenging problem that enables a host of human-computer interaction applications. This paper describes a framework for incremental recognition of human motion that extends the "Condensation" algorithm proposed by Isard and Blake (ECCV'96). Human motions are modeled as temporal trajectories of some estimated parameters over time. The Condensation algorithm uses random sampling techniques to...
A mixed-state Condensation tracker with automatic model-switching
Abstract: There is considerable interest in the computer vision community in representing and modelling motion. Motion models are used as predictors to increase the robustness and accuracy of visual trackers, and as classifiers for gesture recognition. This paper presents a significant development of random sampling methods to allow automatic switching between multiple motion models as a natural extension of the tracking process. The Bayesian mixed-state framework is described in its generality, and the...
A smoothing filter for Condensation
Abstract: Condensation, recently introduced in the computer vision literature, is a particle filtering algorithm which represents a tracked object 's state using an entire probability distribution. Clutter can cause the distribution to split temporarily into multiple peaks, each representing a different hypothesis about the object configuration. When measurements become unambiguous again, all but one peak, corresponding to the true object position, die out. While several peaks persist estimating
CONDENSATION conditional density propagation for visual tracking
Abstract: The problem of tracking curves in dense visual clutter is challenging. Kalman filtering is inadequate because it is based on Gaussian densities which, being unimodal, cannot represent simultaneous alternative hypotheses. The Condensation algorithm uses "factored sampling", previously applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together...
Stochastic simulation algorithms for dynamic probabilistic networks
Abstract:
Stochastic simulation algorithms such as likelihood weighting often give fast, accurate approximations to posterior probabilities in probabilistic networks, and are the methods of choice for very large networks. Unfortunately, the special characteristics of dynamic probabilistic networks (DPNs), which are used to represent stochastic temporal processes, mean that standard simulation algorithms perform very poorly. In essence, the simulation trials diverge further and further from reality as the ...
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