Tracking and data association
Robust multiple car tracking with occlusion reasoning
ECCV '94 Proceedings of the third European conference on Computer vision (vol. 1)
On sequential Monte Carlo sampling methods for Bayesian filtering
Statistics and Computing
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Incremental Learning for Robust Visual Tracking
International Journal of Computer Vision
Sequential mean field variational analysis of structured deformable shapes
Computer Vision and Image Understanding
Adaptive quantized target tracking in wireless sensor networks
Wireless Networks
Modeling and prediction of nonlinear environmental system using Bayesian methods
Computers and Electronics in Agriculture
State and parameter estimation for nonlinear biological phenomena modeled by S-systems
Digital Signal Processing
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The likelihood models used in probabilistic visual tracking applications are often complex non-linear and/or non-Gaussian functions, leading to analytically intractable inference. Solutions then require numerical approximation techniques, of which the particle filter is a popular choice. Particle filters, however, degrade in performance as the dimensionality of the state space increases and the support of the likelihood decreases. As an alternative to particle filters this paper introduces a variational approximation to the tracking recursion. The variational inference is intractable in itself, and is combined with an efficient importance sampling procedure to obtain the required estimates. The algorithm is shown to compare favourably with particle filtering techniques on a synthetic example and two real tracking problems. The first involves the tracking of a designated object in a video sequence based on its colour properties, whereas the second involves contour extraction in a single image.