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Group Activity Recognition based on ARMA Shape Sequence Modeling
Ying Wang; Kaiqi Huang; Tieniu Tan
2007
会议名称IEEE International Conference on Image Processing, 2007
会议录名称IEEE International Conference on Image Processing, 2007
页码209-212
会议日期2007-09-01
会议地点 San Antonio, Texas, USA
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.
关键词Autoregressive Moving Average Processes   feature Extraction
语种英语
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/12717
专题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],2007:209-212.
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