Model-Based Hand Tracking Using a Hierarchical Bayesian Filter

This paper sets out a tracking framework, which is applied to the recovery of threedimensional 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.

• Type: Journal
• Year: 2006
• Where: PAMI
• Authors: Björn Stenger, Arasanathan Thayananthan, Philip H. S. Torr, Roberto Cipolla
• Keywords: Hand Motion, Image Sequences, PAMI 2006, Threedimensional Hand Motion