This paper deals with the problem of multiple object tracking with the condensation algorithm, applied to tracking of soccer players. To solve the problem of failures in tracking multiple players under overlapping, we...
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This paper deals with the problem of multiple object tracking with the condensation algorithm, applied to tracking of soccer players. To solve the problem of failures in tracking multiple players under overlapping, we introduce occlusion alarm probability, which attracts or repels particles based on their posterior distribution of previous time step. Real experiments showed a robust performance.
Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents the integration of color di...
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Robust real-time tracking of non-rigid objects is a challenging task. Particle filtering has proven very successful for non-linear and non-Gaussian estimation problems. The article presents the integration of color distributions into particle filtering, which has typically been used in combination with edge-based image features. Color distributions are applied, as they are robust to partial occlusion, are rotation and scale invariant and computationally efficient. As the color of an object can vary over time dependent on the illumination, the visual angle and the camera parameters, the target model is adapted during temporally stable image observations. An initialization based on an appearance condition is introduced since tracked objects may disappear and reappear. Comparisons with the mean shift tracker and a combination between the mean shift tracker and Kalman filtering show the advantages and limitations of the new approach. (C) 2002 Elsevier Science B.V. All rights reserved.
The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required....
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The Kalman filter provides an effective solution to the linear Gaussian filtering problem. However where there is nonlinearity, either in the model specification or the observation process, other methods are required. Methods known generically as 'particle filters' are considered. These include the condensation algorithm and the Bayesian bootstrap or sampling importance resampling (SIR) filter. These filters represent the posterior distribution of the state variables by a system of particles which evolves and adapts recursively as new information becomes available. In practice, large numbers of particles may be required to provide adequate approximations and for certain applications, after a sequence of updates, the particle system will often collapse to a single point. A method of monitoring the efficiency of these filters is introduced which provides a simple quantitative assessment of sample impoverishment and the authors show how to construct improved particle filters that are both structurally efficient in terms of preventing the collapse of the particle system and computationally efficient in their implementation. This is illustrated with the classic bearings-only tracking problem.
The purpose of this Note is to discuss practical implementation aspects of dynamic substructuring method related to the algorithm used to generate transformation vectors and the corresponding evaluation of error norms...
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The purpose of this Note is to discuss practical implementation aspects of dynamic substructuring method related to the algorithm used to generate transformation vectors and the corresponding evaluation of error norms. A new computational variant that combines the subspace generation of load-dependent vectors and the static condensation technique is developed to compute global modes which are not a summation of local modes or a combination of substructure modes. Finally, a numerical example of a simple cantilever structure is presented to illustrate the relative performance of the proposed solution methods.
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