This paper presents a deep neural-network-based hierarchical graphical model for individual and group activity recognition in surveillance scenes. Deep networks are used to recognize the actions of individual people in a scene. Next, a neural-network-based hierarchical graphical model refines the predicted labels for each class by considering dependencies between the classes. This refinement step mimics a message-passing step similar to inference in a probabilistic graphical model. We show that this approach can be effective in group activity recognition, with the deep graphical model improving recognition rates over baseline methods.
@article{arxiv.1506.04191,
title = {Deep Structured Models For Group Activity Recognition},
author = {Zhiwei Deng and Mengyao Zhai and Lei Chen and Yuhao Liu and Srikanth Muralidharan and Mehrsan Javan Roshtkhari and Greg Mori},
journal= {arXiv preprint arXiv:1506.04191},
year = {2015}
}