Fast online action recognition with efficient structured boosting

In this paper, we propose a novel robust action recognition framework with the following capabilities: 1) online encoding motions to multi-label sequence where the output in each frame is a tuple of labels rather than a single label, 2) providing efficient automatic relevant motion selection framework, 3) learning systems so as to be optimal for online multi-label sequence classification. As for multi-label classification, our approach incorporates contextual information about action not only temporal information but hierarchical information of actions. Inference tends to be complex so as to achieve such complex recognition scheme, however, we propose an efficient Viterbi-like decoding algorithm which integrates forward algorithm and loopy message passing algorithm. As for the learning process, the algorithm optimizes the parameters so as to maximize log likelihood of the model. Boosting, ensemble approach of machine learning, is leveraged to provide efficient feature selection framework in the training process. The experimental results show that the proposed method successfully exploits the impact of contextual information then significantly outperforms the traditional approaches in dynamic gait motion classification.