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2016 | OriginalPaper | Buchkapitel

Motion of Oriented Magnitudes Patterns for Human Action Recognition

verfasst von : Hai-Hong Phan, Ngoc-Son Vu, Vu-Lam Nguyen, Mathias Quoy

Erschienen in: Advances in Visual Computing

Verlag: Springer International Publishing

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Abstract

In this paper, we present a novel descriptor for human action recognition, called Motion of Oriented Magnitudes Patterns (MOMP), which considers the relationships between the local gradient distributions of neighboring patches coming from successive frames in video. The proposed descriptor also characterizes the information changing across different orientations, is therefore very discriminative and robust. The major advantages of MOMP are its very fast computation time and simple implementation. Subsequently, our features are combined with an effective coding scheme VLAD (Vector of locally aggregated descriptors) in the feature representation step, and a SVM (Support Vector Machine) classifier in order to better represent and classify the actions. By experimenting on several common benchmarks, we obtain the state-of-the-art results on the KTH dataset as well as the performance comparable to the literature on the UCF Sport dataset.

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Vorheriges Kapitel A New Method for Fall Detection of Elderly Based on Human Shape and Motion Variation
Nächstes Kapitel Adaptive Video Transition Detection Based on Multiscale Structural Dissimilarity
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Metadaten
Titel
Motion of Oriented Magnitudes Patterns for Human Action Recognition
verfasst von
Hai-Hong Phan
Ngoc-Son Vu
Vu-Lam Nguyen
Mathias Quoy
Copyright-Jahr
2016
Buch
Advances in Visual Computing

Print ISBN: 978-3-319-50831-3

Electronic ISBN: 978-3-319-50832-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-50832-0_17