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Video Recognition of Human Fall Based on Spatiotemporal Features


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Abstract

A systematic framework for recognizing human fall from video is presented in this work. For the foreground extraction, instead of remodeling background of every video frame, we directly extract cuboids that are composed of spatiotemporal interest points detected by separable linear filter from video sequences. We then represent these video patches as local image gradient descriptors with greatly reduced dimensions by principle component analysis (PCA). From labeled video patches, a supervised learning method based on Gaussian RBF kernel is proposed to determine the maximum margin between fall and normal activity, and then a novel video sequence can be categorize into fall or normal activity by an optimal hyperplane. We tested the above method on datasets set up based on the LPO-CV testing paradigm, which verified the proposed method and demonstrated its advantage over other state-of-the-art approaches for fall recognition.


Keywords


Pages

Total Pages: 7
Pages: 303-309

DOI
10.1080/10798587.2015.1095487


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Published

Volume: 22
Issue: 2
Year: 2015

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JOURNAL INFORMATION


ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)
Journal: 1995-Present




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