Combining motion segmentation with tracking for activity analysis

Authors:
Jiang Gao;Alexander G. Hauptmann;Howard D. Wactlar
Affiliations:
School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, Pittsburgh, PA
Venue:
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Year:
2004

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Abstract

We explore a novel motion feature as the appropriate basis for classifying or describing a number of fine motor human activities. Our approach not only estimates motion directions and magnitudes in different image regions, but also provides accurate segmentation of moving regions. Through a combination of motion segmentation and region tracking techniques, while filtering for temporal consistency, we achieve a balance between accuracy and reliability of motion feature extraction. To identify specific activities, we characterize the dominant directions of relative motions. Experimental results show that this approach to motion feature analysis could be successful in assisting caregivers at a nursing home in assessments of patient's activity levels over time.