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.

Here, the authors introduce a novel system which incorporates the discriminative motion of oriented magnitude patterns (MOMP) descriptor into simple yet efficient techniques. The authors' descriptor both investigates the relations of the local gradient distributions in neighbours among consecutive image sequences and characterises information changing across different orientations. The proposed system has two main contributions: (i) the authors adopt feature post-processing principal component analysis followed by vector of locally aggregated descriptors encoding to de-correlate MOMP descriptor and reduce the dimension in order to speed up the algorithm; (ii) then the authors include the feature selection (i.e. statistical dependency, mutual information, and minimal redundancy maximal relevance) to find out the best feature subset to improve the performance and decrease the computational expense in classification through support vector machine techniques. Experiment results on four data sets, Weizmann (98.4%), KTH (96.3%), UCF Sport (82.0%), and HMDB51 (31.5%), prove the efficiency of the authors' algorithm.

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