Accession Number:

ADA505847

Title:

Real-Time Head Pose Estimation Using a WEBCAM: Monocular Adaptive View-Based Appearance Model

Descriptive Note:

Conference paper

Corporate Author:

UNIVERSITY OF SOUTHERN CALIFORNIA MARINA DEL REY CA INST FOR CREATIVE TECHNOLOGIES

Personal Author(s):

• Morency, Louis-Philippe

Report Date:

2008-12-01

Pagination or Media Count:

9.0

Abstract:

Accurately estimating the persons 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.

Descriptors:

• *PATTERN RECOGNITION
• *VIEWERS
• *CAMERAS
• *HEAD(ANATOMY)
• *REAL TIME
• *VIDEO IMAGES
• TRACKING
• LONG RANGE(TIME)
• ESTIMATES
• THREE DIMENSIONAL
• DRIFT
• VISION
• INERTIAL SYSTEMS
• MAGNETIC DETECTORS
• IMAGE MOTION COMPENSATION
• ROBOTS
• INTERACTIONS
• HUMANS
• DETECTORS
• SYMPOSIA
• REPRINTS
• POSITION(LOCATION)

Subject Categories:

• Cybernetics
• Photography
• Miscellaneous Detection and Detectors
• Anatomy and Physiology
• Command, Control and Communications Systems

Distribution Statement:

APPROVED FOR PUBLIC RELEASE