Hierarchical Graph-Rnns for Action Detection of Multiple Activities

Sovan Biswas, Yaser Souri, Juergen Gall

Abstract: In this paper, we propose an approach that spatially lo-calizes the activities in a video frame where each person can perform multiple activities at the same time. Our approach takes the temporal scene context as well as the relations of the actions of detected persons into account. While the temporal context is modeled by a temporal recurrent neural network (RNN), the relations of the actions are modeled by a graph RNN. Both networks are trained together and the proposed approach achieves state of the art results on the AVA dataset.