A framework for spatiotemporal control in the tracking of visual contours
International Journal of Computer Vision
Contour Tracking by Stochastic Propagation of Conditional Density
ECCV '96 Proceedings of the 4th European Conference on Computer Vision-Volume I - Volume I
ICONDENSATION: Unifying Low-Level and High-Level Tracking in a Stochastic Framework
ECCV '98 Proceedings of the 5th European Conference on Computer Vision-Volume I - Volume I
Elliptical Head Tracking Using Intensity Gradients and Color Histograms
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Particle filters for positioning, navigation, and tracking
IEEE Transactions on Signal Processing
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Towards human motion capture from a camera mounted on a mobile robot
Image and Vision Computing
Comparison of stochastic filtering methods for 3D tracking
Pattern Recognition
Recent advances and trends in visual tracking: A review
Neurocomputing
Tracking-by-detection of multiple persons by a resample-move particle filter
Machine Vision and Applications
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Bayesian particle filters have become popular for tracking human motion in cluttered scenes. The most commonly used filters suffer from two drawbacks. First, the prior used for the filtering step is often poor due to relatively large, poorly modelled inter-frame motion. Second, the use of the prior as an importance function results in inefficient sampling of the posterior. The use of the auxiliary particle filter (APF) and the novel iterated likelihood weighting filter (ILW) are proposed here in order to help address these problems. Experimental results comparing the filters' accuracy and consistency are presented for a scenario in which a person is tracked in an overhead view using an ellipse model. A likelihood model based on combined region (colour) and boundary (gradient) cues is motivated and used. The ILW filter is shown to outperform both Condensation and the APF on typical sequences from this scenario.