A Hierarchical Deep Temporal Model for Group Activity Recognition
Abstract
In group activity recognition, the temporal dynamics of the whole activity can be inferred based on the dynamics of the individual people representing the activity. We build a deep model to capture these dynamics based on LSTM (long short-term memory) models. To make use of these observations, we present a 2-stage deep temporal model for the group activity recognition problem. In our model, a LSTM model is designed to represent action dynamics of individual people in a sequence and another LSTM model is designed to aggregate person-level information for whole activity understanding. We evaluate our model over two datasets: the Collective Activity Dataset and a new volleyball dataset. Experimental results demonstrate that our proposed model improves group activity recognition performance compared to baseline methods.
Cite
Text
Ibrahim et al. "A Hierarchical Deep Temporal Model for Group Activity Recognition." Conference on Computer Vision and Pattern Recognition, 2016. doi:10.1109/CVPR.2016.217Markdown
[Ibrahim et al. "A Hierarchical Deep Temporal Model for Group Activity Recognition." Conference on Computer Vision and Pattern Recognition, 2016.](https://mlanthology.org/cvpr/2016/ibrahim2016cvpr-hierarchical/) doi:10.1109/CVPR.2016.217BibTeX
@inproceedings{ibrahim2016cvpr-hierarchical,
title = {{A Hierarchical Deep Temporal Model for Group Activity Recognition}},
author = {Ibrahim, Mostafa S. and Muralidharan, Srikanth and Deng, Zhiwei and Vahdat, Arash and Mori, Greg},
booktitle = {Conference on Computer Vision and Pattern Recognition},
year = {2016},
doi = {10.1109/CVPR.2016.217},
url = {https://mlanthology.org/cvpr/2016/ibrahim2016cvpr-hierarchical/}
}