A group of persons can be analyzed at various semantic levels such as individual actions, their interactions, and the activity of the entire group. In this paper, we propose a structural recurrent neural network (SRNN) that uses a series of interconnected RNNs to jointly capture the actions of individuals, their interactions, as well as the group activity. While previous structural recurrent neural networks assumed that the number of nodes and edges is constant, we use a grid pooling layer to address the fact that the number of individuals in a group can vary. We evaluate two variants of the structural recurrent neural network on the Volleyball Dataset.
In REF , the authors introduced the Structural Recurrent Neural Network (SRNN) model which is able to handle a varying number of individuals in the scene at each time step with the aid of a grid pooling layer.
Structural Recurrent Neural Network (SRNN) for Group Activity Analysis
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
2018 IEEE Winter Conference on Applications of Computer Vision (WACV)
Computer Science