Work

Model-based hand tracking using a hierarchical Bayesian filter

Year: 2006

Type: article

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.

Source: IEEE Transactions on Pattern Analysis and Machine Intelligence

DOI: 10.1109/tpami.2006.189

Authors: B. Stenger, A. Thayananthan, P.H.S. Torr, R. Cipolla

Institutions: Toshiba (Japan), University of Cambridge, Oxford Brookes University, Institute of Electrical and Electronics Engineers

Language: English

Topic: Hand Gesture Recognition Systems

Subfield: Human-Computer Interaction

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: Gates Cambridge Trust, Engineering and Physical Sciences Research Council

OpenAlex ID: W2100428290

FWCI: 38.41

Cites: 163

Cited by: 418

Related to: 8