Discriminative Action States Discovery for Online Action Recognition

In this paper, we provide an approach for online human action recognition, where the videos are represented by frame-level descriptors. To address the large intraclass variations of frame-level descriptors, we propose an action states discovery method to discover the different distributions of frame-level descriptors while training a classifier. A positive sample set is treated as multiple clusters called action states. The action states model can be effectively learned by clustering the positive samples and optimizing the decision boundary of each state simultaneously. Experimental results show that our method not only outperforms the state-of-the-art methods, but also can predict the video by an on-going process with a real-time speed.