@inproceedings{1fc1af883f874b41a64f42848fb85225,
title = "A group contextual model for activity recognition in crowded scenes",
abstract = "This paper presents an efficient framework for activity recognition based on analyzing group context in crowded scenes. We use graph based clustering algorithm to discover interacting groups using top-down mechanism. Using discovered interacting groups, we propose a new group context activity descriptor capturing not only the focal person's activity but also behaviors of its neighbors. For a high-level of understanding of human activities, we propose a random field model to encode activity relationships between people in the scene. We evaluate our approach on two public benchmark datasets. The results of both the steps show that our method achieves recognition rates comparable to state-of-the-art methods for activity recognition in crowded scenes.",
keywords = "Activity recognition, Group context activity, Social interaction",
author = "Tran, {Khai N.} and Xu Yan and Kakadiaris, {Ioannis A.} and Shah, {Shishir K.}",
year = "2015",
doi = "10.5220/0005258600050012",
language = "English (US)",
series = "VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications; VISIGRAPP, Proceedings",
publisher = "SciTePress",
pages = "5--12",
editor = "Jose Braz and Sebastiano Battiato and Francisco Imai",
booktitle = "VISAPP 2015 - 10th International Conference on Computer Vision Theory and Applications; VISIGRAPP, Proceedings",
note = "10th International Conference on Computer Vision Theory and Applications, VISAPP 2015 ; Conference date: 11-03-2015 Through 14-03-2015",
}