Human action recognition in videos using hybrid motion features

In this paper, we present hybrid motion features to promote action recognition in videos. The features are composed of two complementary components from different views of motion information. On one hand, the period feature is extracted to capture global motion in time-domain. On the other hand, the enhanced histograms of motion words (EHOM) are proposed to describe local motion information. Each word is represented by optical flow of a frame and the correlations between words are encoded into the transition matrix of a Markov process, and then its stationary distribution is extracted as the final EHOM. Compared to traditional Bags of Words representation, EHOM preserves not only relationships between words but also temporary information in videos to some extent. We show that by integrating local and global features, we get improved recognition rates on a variety of standard datasets.