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In real life, group activity recognition plays a significant and fundamental role in a variety of applications, e.g. sports video analysis, abnormal behavior detection, and intelligent surveillance. In a complex dynamic scene, a crucial yet challenging issue is how to better model the spatio-temporal contextual information and inter-person relationship. In this paper, we present a novel attentive semantic recurrent neural network (RNN), namely, stagNet, for understanding group activities and individual actions in videos, by combining the spatio-temporal attention mechanism and semantic graph modeling. Specifically, a structured semantic graph is explicitly modeled to express the spatial contextual content of the whole scene, which is further incorporated with the temporal factor through structural-RNN. By virtue of the “factor sharing” and “message passing” mechanisms, our stagNet is capable of extracting discriminative …