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Exploiting Self-Adaptive Posture-based Focus Estimation for Lecture Video Editing ABSTRACT
| Citations: | 2 - 2 self |
BibTeX
@MISC{Wang_exploitingself-adaptive,
author = {Feng Wang},
title = {Exploiting Self-Adaptive Posture-based Focus Estimation for Lecture Video Editing ABSTRACT},
year = {}
}
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Abstract
Head pose plays a special role in estimating a presenter’s focuses and actions for lecture video editing. This paper presents an efficient and robust head pose estimation algorithm to cope with the new challenges arising in the content management of lecture videos. These challenges include speed requirement, low video quality, variant presenting styles and complex settings in modern classrooms. Our algorithm is based on a robust hierarchical representation of skin color clustering and a set of pose templates that are automatically trained. Contextual information is also considered to refine pose estimation. Most importantly, we propose an online learning approach to deal with different presenting styles, which has not been addressed before. We show that the proposed approach can significantly improve the performance of pose estimation. In addition, we also describe how posture is used in focus estimation for lecture video editing by integrating with gesture.
Keyphrases
lecture video editing abstract lecture video self-adaptive posture-based focus estimation pose estimation special role new challenge skin color clustering robust head pose estimation algorithm modern classroom focus estimation pose template variant presenting style speed requirement complex setting contextual information presenter focus different presenting style low video quality content management robust hierarchical representation
