Skeleton-based Relational Reasoning for Group Activity Analysis

Research on group activity recognition mostly leans on the standard two-stream approach (RGB and Optical Flow) as their input features. Few have explored explicit pose information, with none using it directly to reason about the persons interactions. In this paper, we leverage the skeleton information to learn the interactions between the individuals straight from it. With our proposed method GIRN, multiple relationship types are inferred from independent modules, that describe the relations between the body joints pair-by-pair. Additionally to the joints relations, we also experiment with the previously unexplored relationship between individuals and relevant objects (e.g. volleyball). The individuals distinct relations are then merged through an attention mechanism, that gives more importance to those individuals more relevant for distinguishing the group activity. We evaluate our method in the Volleyball dataset, obtaining competitive results to the state-of-the-art. Our experiments demonstrate the potential of skeleton-based approaches for modeling multi-person interactions.Comment: 26 pages, 5 figures, accepted manuscript in Elsevier Pattern Recognition, minor writing revisions and new reference

Recognition and detection of two-person interactive actions using automatically selected skeleton features

© 2013 IEEE. Recognition and detection of interactive actions performed by multiple persons have a wide range of real-world applications. Existing studies on the human activity analysis focus mainly on classifying video clips of simple actions performed by a single person, whereas the problem of understanding complex human activities with causal relationships between two people has not been sufficiently addressed yet. In this paper, we employ systematically organized skeleton features enhanced with directional features, and utilize sparse-group lasso to automatically choose discriminative factors that help in dealing with interactive action recognition and real-time detection tasks. Experiments on two person interaction datasets demonstrate the superiority of our approach to the state-of-the-art methods