CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
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
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International Journal of Computer Vision
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IEEE Transactions on Pattern Analysis and Machine Intelligence
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Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
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Computer Vision and Image Understanding
A real-time hand tracker using variable-length Markov models of behaviour
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CamShift guided particle filter for visual tracking
Pattern Recognition Letters
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ACM SIGGRAPH 2009 papers
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ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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A multi-view vision-based hand motion capturing system
Pattern Recognition
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A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Framework for vision-based sensory games using motion estimation and collision responses
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Finding the convex hull of a simple polygon
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IEEE Transactions on Image Processing
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IEEE Transactions on Image Processing
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This paper presents a gravity optimised particle filter (GOPF) where the magnitude of the gravitational force for every particle is proportional to its weight. GOPF attracts nearby particles and replicates new particles as if moving the particles towards the peak of the likelihood distribution, improving the sampling efficiency. GOPF is incorporated into a technique for hand features tracking. A fast approach to hand features detection and labelling using convexity defects is also presented. Experimental results show that GOPF outperforms the standard particle filter and its variants, as well as state-of-the-art CamShift guided particle filter using a significantly reduced number of particles.