CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Introduction to Robotics: Mechanics and Control
Introduction to Robotics: Mechanics and Control
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
Implicit Probabilistic Models of Human Motion for Synthesis and Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Face Recognition from Video: A CONDENSATION Approach
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Mode-based Multi-Hypothesis Head Tracking Using Parametric Contours
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Head Tracking by Active Particle Filtering
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Modelling and estimating the pose of a human arm
Machine Vision and Applications - Special issue: Human modeling, analysis, and synthesis
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Sequential Monte Carlo (SMC) methods have in recent years been applied to handle some of the problems inherent to model-based tracking. In this paper we suggest to apply bootstrapping to reduce the required number of particles in SMC tracking. By bootstrapping is meant to track reliable low-level image features and use them to bootstrap the high-level model-based tracking. The concept of bootstrapped SMC tracking is exemplified by monocular tracking of the 3D pose of a human arm with the position of the hand in the image as the bootstrapping information. Tests suggest that both bootstrapping is a sound strategies and an improvement over standard SMC-methods.