The availability of large-scale video action understanding datasets has facilitated advances in the interpretation of visual scenes containing people. However, learning to recognize human activities in an unconstrained real-world environment, with potentially highly unbalanced and long-tailed distributed data remains a significant challenge, not least owing to the lack of a reflective large-scale dataset. Most existing large-scale datasets are either collected from a specific or constrained environment, e.g. kitchens or rooms, or video sharing platforms such as YouTube. In this paper, we introduce JRDB-Act, a multi-modal dataset, as an extension of the existing JRDB, which is captured by asocial mobile manipulator and reflects a real distribution of human daily life actions in a university campus environment. JRDB-Act has been densely annotated with atomic actions, comprises over 2.8M action labels, constituting a large-scale spatio-temporal action detection dataset. Each human bounding box is labelled with one pose-based action label and multiple (optional) interaction-based action labels. Moreover JRDB-Act comes with social group identification annotations conducive to the task of grouping individuals based on their interactions in the scene to infer their social activities (common activities in each social group).
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