@inproceedings{8aeb55f2d8424d37956b46cf50cefff3,
title = "Group activity recognition based on ARMA shape sequence modeling",
abstract = "In this paper, we propose a system identification approach for group activity recognition in traffic surveillance. Statistical shape theory is used to extract features, and then ARMA (Autoregressive and Moving Average) is adopted for feature learning and activity identification. Here only a few points, instead of the complete trajectory of each object are used to describe the dynamic information of group activity. And ARMA is employed to learn activity sequences. The performance of the proposed method is proved by experiments on 570 video sequences, with the average recognition rate of 88% (compared with 81% of HMM). The extracted features are invariant to zoom, pan and tilt, which is also proved in the experiments.",
keywords = "ARMA, Group activity, Landmark, Shape theory, Surveillance",
author = "Ying Wang and Kaiqi Huang and Tieniu Tan",
year = "2007",
doi = "10.1109/ICIP.2007.4379283",
language = "English",
isbn = "1424414377",
volume = "3",
series = "International Conference on Image Processing Proceedings",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
pages = "209--212",
booktitle = "2007 IEEE International Conference on Image Processing",
address = "United States",
note = "14th IEEE International Conference on Image Processing, ICIP 2007 ; Conference date: 16-09-2007 Through 19-09-2007",
}