Model-based hand tracking using a hierarchical Bayesian filter

Citations Over TimeTop 1% of 2006 papers

Abstract

This paper sets out a tracking framework, which is applied to the recovery of three-dimensional hand motion from an image sequence. The method handles the issues of initialization, tracking, and recovery in a unified way. In a single input image with no prior information of the hand pose, the algorithm is equivalent to a hierarchical detection scheme, where unlikely pose candidates are rapidly discarded. In image sequences, a dynamic model is used to guide the search and approximate the optimal filtering equations. A dynamic model is given by transition probabilities between regions in parameter space and is learned from training data obtained by capturing articulated motion. The algorithm is evaluated on a number of image sequences, which include hand motion with self-occlusion in front of a cluttered background.

Related Papers

• → Reducing Neural Network Parameter Initialization Into an SMT Problem (Student Abstract)(2021)2 cited
• → A New Initialization Method for Neural Networks with Weight Sharing(2021)2 cited
• → Remarks on the initialization of Caputo derivative(2012)4 cited
• The Distributed Initialization Algorithm Based on Known n MSs(2004)
• → Comparison of Random Weight Initialization to New Weight Initialization CONEXP(2020)