Paper Info
Title
Generalized adaptive view-based appearance model: Integrated framework for monocular head pose estimation
Abstract
Accurately estimating the person's head position and orientation is an important task for a wide range of applications such as driver awareness and human-robot interaction. Over the past two decades, many approaches have been suggested to solve this problem, each with its own advantages and disadvantages. In this paper, we present a probabilistic framework called generalized adaptive viewbased appearance model (GAVAM) which integrates the advantages from three of these approaches: (1) the automatic initialization and stability of static head pose estimation, (2) the relative precision and user-independence of differential registration, and (3) the robustness and bounded drift of keyframe tracking. In our experiments, we show how the GAVAM model can be used to estimate head position and orientation in real-time using a simple monocular camera. Our experiments on two previously published datasets show that the GAVAM framework can accurately track for a long period of time (>2 minutes) with an average accuracy of 3.5deg and 0.75 in with an inertial sensor and a 3D magnetic sensor.
Year
DOI
Venue
2008
10.1109/AFGR.2008.4813429
Amsterdam
Keywords
Field
DocType
pose estimation,probability,generalized adaptive view-based appearance model,monocular head pose estimation,probabilistic framework
Inertial frame of reference,Computer vision,Computer science,Robustness (computer science),Pose,Active appearance model,Artificial intelligence,Initialization,Monocular,Bounded function,Probabilistic framework
Conference
ISSN
ISBN
Citations 
2326-5396
978-1-4244-2154-1
47
PageRank 
References 
Authors
2.98
19
3
Name
Order
Citations
PageRank
Louis-Philippe Morency13220200.79
Jacob Whitehill298858.75
Javier R. Movellan31853150.44