People in seats counting via seat detection for meeting surveillance
文献类型：会议论文
| 作者 | Hongyu Liang; Jinchen Wu; Kaiqi Huang
|
| 出版日期 | 2012 |
| 会议日期 | 2012 |
| 会议地点 | China |
| 关键词 | People In Seats countIng meeting Surveillance coarse-to-fine Classification |
| 页码 | 202–210 |
| 英文摘要 | People in seats counting is very important for meeting surveillance. While as a canonical pattern recognition problem, it鈥檚 very difficult due to various appearances of people and other outliers such as bags and clothes. To solve this problem we propose a coarse-to-fine framework. Firstly, we use the coarse classification module to retrieve the completely empty seats. To overcome the influence of noises caused by shadows and light spots, we fuse multiple global features calculated by background subtraction. Then in the fine classification module, a proposed SW-HOG feature and the LBP feature are combined together to solve the problem of occlusion and make sure the classification is real time. Finally a time-related calibration module is applied to suppress some outliers across frames with condition that the video frames are not successive. Experiments on a real meeting dataset demonstrate that the accuracy of the proposed method reaches 99.88%. |
| 会议录 | Chinese Conference on Pattern Recognition
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/12690]  |
| 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 | Hongyu Liang,Jinchen Wu,Kaiqi Huang. People in seats counting via seat detection for meeting surveillance[C]. 见:. China. 2012. |
入库方式： OAI收割
来源：自动化研究所
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