Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint

Item

Title
en_US Stereo-Based Head Pose Tracking Using Iterative Closest Point and Normal Flow Constraint
Creator
en_US Morency, Louis-Philippe
Date
2004-10-20T20:31:42Z
Date Available
2004-10-20T20:31:42Z
Date Issued
en_US 2003-05-01
Identifier
en_US AITR-2003-006
uri
http://hdl.handle.net/1721.1/7102
Abstract
en_US In this text, we present two stereo-based head tracking techniques along with a fast 3D model acquisition system. The first tracking technique is a robust implementation of stereo-based head tracking designed for interactive environments with uncontrolled lighting. We integrate fast face detection and drift reduction algorithms with a gradient-based stereo rigid motion tracking technique. Our system can automatically segment and track a user's head under large rotation and illumination variations. Precision and usability of this approach are compared with previous tracking methods for cursor control and target selection in both desktop and interactive room environments. The second tracking technique is designed to improve the robustness of head pose tracking for fast movements. Our iterative hybrid tracker combines constraints from the ICP (Iterative Closest Point) algorithm and normal flow constraint. This new technique is more precise for small movements and noisy depth than ICP alone, and more robust for large movements than the normal flow constraint alone. We present experiments which test the accuracy of our approach on sequences of real and synthetic stereo images. The 3D model acquisition system we present quickly aligns intensity and depth images, and reconstructs a textured 3D mesh. 3D views are registered with shape alignment based on our iterative hybrid tracker. We reconstruct the 3D model using a new Cubic Ray Projection merging algorithm which takes advantage of a novel data structure: the linked voxel space. We present experiments to test the accuracy of our approach on 3D face modelling using real-time stereo images.
Extent
en_US 60 p.
5276045 bytes
2896854 bytes
Format
application/postscript
application/pdf
Language
en_US
Relation
en_US AITR-2003-006
Subject
en_US AI
en_US Head pose estimation
en_US Stereo processing
en_US Cursor control
en_US 3D model acquisition
Item sets
AI Technical Reports (1964 - 2004)