Group Activity Recognition based on ARMA Shape Sequence Modeling
文献类型：会议论文
| 作者 | Ying Wang; Kaiqi Huang ; Tieniu Tan
|
| 出版日期 | 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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