Benchmarking a Multimodal and Multiview and Interactive Dataset for Human Action Recognition

Human action recognition is an active research area in both computer vision and machine learning communities. In the past decades, the machine learning problem has evolved from conventional single-view learning problem, to cross-view learning, cross-domain learning and multitask learning, where a large number of algorithms have been proposed in the literature. Despite having large number of action recognition datasets, most of them are designed for a subset of the four learning problems, where the comparisons between algorithms can further limited by variances within datasets, experimental configurations, and other factors. To the best of our knowledge, there exists no dataset that allows concurrent analysis on the four learning problems. In this paper, we introduce a novel multimodal and multiview and interactive (M2I) dataset, which is designed for the evaluation of human action recognition methods under all four scenarios. This dataset consists of 1760 action samples from 22 action categories, including nine person-person interactive actions and 13 person-object interactive actions. We systematically benchmark state-of-the-art approaches on M2I dataset on all four learning problems. Overall, we evaluated 13 approaches with nine popular feature and descriptor combinations. Our comprehensive analysis demonstrates that M2I dataset is challenging due to significant intraclass and view variations, and multiple similar action categories, as well as provides solid foundation for the evaluation of existing state-of-the-art algorithms.