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Group Activity Recognition based on ARMA Shape Sequence Modeling
Ying Wang; Kaiqi Huang; Tieniu Tan
2007
Conference NameIEEE International Conference on Image Processing, 2007
Source PublicationIEEE International Conference on Image Processing, 2007
Pages209-212
Conference Date2007-09-01
Conference Place San Antonio, Texas, USA
AbstractIn 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.
KeywordAutoregressive Moving Average Processes   feature Extraction
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/12717
Collection智能感知与计算研究中心
Corresponding AuthorKaiqi Huang
Affiliation中国科学院自动化研究所
Recommended Citation
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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