CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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
Evaluating Multi-Object Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops - Volume 03
Rao-Blackwellised particle filter for colour-based tracking
Pattern Recognition Letters
Recent advances and trends in visual tracking: A review
Neurocomputing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Adaptive Object Tracking Based on an Effective Appearance Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Intelligent multi-camera video surveillance: A review
Pattern Recognition Letters
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Colour histogram based particle filter is an efficient technique for tracking. The scale of kernel is a crucial parameter which is formed by the weighted particles. However, changing the scale simply by the histogram similarity sometimes results in the kernel fast shrinks into local optimal. Therefore, a method is proposed to detect the potential hints of shrinking first. Then, the centre distribution of particles is utilized to resize the kernel. The similarity likelihood between modified kernel and the ground truth is used for forming the resampling step to solve the degeneracy problem. We test the proposed approach on both simulated and real scenarios. In these experiments, the method can efficiently solve the problem of shrinking.