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Recognizing Human Actions by a Bag of Visual Words

In this paper a novel method for action recognition based on the bag of visual words approach is proposed. The main contribution is to model each action through a high level features vector computed as the histogram of the visual words: the visual words are extracted by analyzing global descriptors of the scene and their occurrences are evaluated according to a codebook, a kind of dictionary, which encodes the typical visual words, automatically extracted during the learning phase. The classification is performed by using an SVM classifier, trained only by using high level features vectors, in order to increase the overall reliability of the system. The experimentation has been conducted over two recently proposed datasets, the MIVIA and the MHAD, the promising results confirm the robustness and the stability of the proposed approach.