@inproceedings{4637429978fc4cdd9fd2b1dbdc8585d3,
title = "Local group relationship analysis for group activity recognition",
abstract = "In this paper, we present an approach that exploits local group relationship to tackle the human group activity recognition problem. Specifically, rather than analyze every human motion, we first grouping individual human object into local groups to represent the relationship in the overall scene. The important movement information is maximized by modeling both each human motion and local group relationships. The gated recurrent unit model has been adopted to handle an arbitrary length of trajectory information with non-linear hidden units. In our experiment on public human group activity dataset, we compared the performance of proposed method with that of other competing methods and showed that the proposed method outperforms others.",
keywords = "Gated recurrent unit, Group activity recognition, Local group relationship, Video surveillance",
author = "Lee, {Dong Gyu} and Kim, {Pil Soo} and Lee, {Seong Whan}",
note = "Publisher Copyright: {\textcopyright} 2017 Institute of Control, Robotics and Systems - ICROS.; 17th International Conference on Control, Automation and Systems, ICCAS 2017 ; Conference date: 18-10-2017 Through 21-10-2017",
year = "2017",
month = dec,
day = "13",
doi = "10.23919/ICCAS.2017.8204447",
language = "English",
series = "International Conference on Control, Automation and Systems",
publisher = "IEEE Computer Society",
pages = "236--238",
booktitle = "ICCAS 2017 - 2017 17th International Conference on Control, Automation and Systems - Proceedings",
address = "United States",
}