Probability-based method for boosting human action recognition using scene context

In this study, the authors investigate the possibility of boosting action recognition performance by exploiting the associated scene context. Towards this end, the authors model a scene as a mid-level `middle layer' in order to bridge action descriptors and action categories. This is achieved via a scene topic model, in which hybrid visual descriptors, including spatial-temporal action features and scene descriptors, are first extracted from a video sequence. Then, the authors learn a joint probability distribution between scene and action using a naive Bayes nearest neighbour algorithm, which is adopted to jointly infer the action categories online by combining off-the-shelf action recognition algorithms. The authors demonstrate the advantages of their approach by comparing it with state-of-the-art approaches using several action recognition benchmarks.