A Real-time System for Head Tracking and Pose Estimation

People

Zengyin Zhang
Minyoung Kim
Fernando de la Torre
Wende Zhang

Abstract

Driver's visual attention provides important clues about his/her activities and awareness. To monitor driver's awareness, this paper proposes a real-time person-independent head tracking and pose estimation system using a monochromatic camera. The tracking and head-pose estimation tasks are formulated as regression problems. Three regression methods are proposed: (i) individual mapping on images for head tracking, (ii) direct mapping to subspace for head tracking, which predicts a subspace from one sample, and (iii) semantic piecewise regression for head-pose estimation. The approaches are evaluated on standard databases, and on several videos collected in vehicle environments.

Citation

Zengyin Zhang, Minyoung Kim, Fernando de la Torre and Wende Zhang
A Real-time System for Head Tracking and Pose Estimation
Workshop on Signal, Gesture and Activity. In conjunction with ECCV 2010
[PDF] [Bibtex]

Results

Acknowledgements and Funding

This research is supported by:

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