Prediction with Gaussian processes: from linear regression to linear prediction and beyond
Learning in graphical models
Sparse on-line Gaussian processes
Neural Computation
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential Monte Carlo Methods to Train Neural Network Models
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
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Particle degeneracy is one of the main problems when particle filters are applied to visual tracking. The effective solution methods on the degeneracy phenomenon include good choice of proposal distribution and use of resampling. In this paper, we propose a novel visual-tracking algorithm using particle filters with Gaussian process regression and resampling techniques, which effectively abate the influence of particle degeneracy and improve the robustness of visual tracking. The main characteristic of the proposed algorithm is that we incorporate particle filters with Gaussian process regression which can learn highly effective proposal distributions for particle filters to track the visual objects. Experimental results in challenging sequences demonstrate the effectiveness and robustness of the proposed method.