CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The Tinmith system: demonstrating new techniques for mobile augmented reality modelling
AUIC '02 Proceedings of the Third Australasian conference on User interfaces - Volume 7
Stochastic Algorithms for Visual Tracking: Probabilistic Modelling and Stochastic Algorithms for Visual Localisation and Tracking
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
WearTrack: A Self-Referenced Head and Hand Tracker for Wearable Computers and Portable VR
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Deictic gestures with a time-of-flight camera
GW'09 Proceedings of the 8th international conference on Gesture in Embodied Communication and Human-Computer Interaction
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In modern society there is an increasing demand to access, record and manipulate large amounts of information. This has inspired a new approach to thinking about and designing personal computers, where the ultimate goal is to produce a truly wearable computer. In this work we present a non-invasive hand-gesture recognition system aimed at deictic gestures. Our system is based on the powerful Sequential Monte Carlo framework which is enhanced with respect to increased robustness. This is achieved by using ratios in the likelihood function together with two image cues: edges and skin color. The system proves to be fast, robust towards noise, and quick to lock on to the object (hand). All of which is achieved without the use of special lighting or special markers on the hands, hence our system is a non-invasive solution.