Group Activity Recognition based on ARMA Shape Sequence Modeling

	Group Activity Recognition based on ARMA Shape Sequence Modeling
	Ying Wang; Kaiqi Huang; Tieniu Tan
	2007
会议日期	2007-09-01
会议地点	San Antonio, Texas, USA
关键词	Autoregressive Moving Average Processes feature Extraction
页码	209-212
英文摘要	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.
会议录	IEEE International Conference on Image Processing, 2007
语种	英语
内容类型	会议论文
源URL	[http://ir.ia.ac.cn/handle/173211/12717]
专题	自动化研究所_智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Ying Wang,Kaiqi Huang,Tieniu Tan. Group Activity Recognition based on ARMA Shape Sequence Modeling[C]. 见:. San Antonio, Texas, USA. 2007-09-01.