  In this work, we present a framework based on multi-stream convolutional
neural networks (CNNs) for group activity recognition. Streams of CNNs are
separately trained on different modalities and their predictions are fused at
the end. Each stream has two branches to predict the group activity based on
person and scene level representations. A new modality based on the human pose
estimation is presented to add extra information to the model. We evaluate our
method on the Volleyball and Collective Activity datasets. Experimental results
show that the proposed framework is able to achieve state-of-the-art results
when multiple or single frames are given as input to the model with 90.50% and
86.61% accuracy on Volleyball dataset, respectively, and 87.01% accuracy of
multiple frames group activity on Collective Activity dataset.
