Real-Time Head Pose Estimation Using a WEBCAM: Monocular Adaptive View-Based Appearance Model
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 Monocular Adaptive View-based Appearance Model (MAVAM) which integrates the advantages from two of these approaches: (1) the relative precision and user-independence of differential registration, and (2) the robustness and bounded drift of keyframe tracking. In our experiments, we show how the MAVAM 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 MAVAM framework can accurately track for a long period of time (more than 2 minutes) with an average accuracy of 3.9 degrees and 1.2 inches with an inertial sensor and a 3D magnetic sensor.
Document Details
- Document Type
- Technical Report
- Publication Date
- Dec 01, 2008
- Accession Number
- ADA505847
Entities
People
- Louis-Philippe Morency
Organizations
- University of Southern California