Motion of oriented magnitudes patterns for Human Action Recognition

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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Hai-Hong Phan, Ngoc-Son Vu, Vu-Lam Nguyen, Mathias Quoy. Motion of oriented magnitudes patterns for Human Action Recognition. International Symposium on Visual Computing, Dec 2016, Las Vegas, United States. ⟨10.1007/978-3-319-50832-0_17⟩. ⟨hal-02265245⟩



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