People in seats counting via seat detection for meeting surveillance

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	People in seats counting via seat detection for meeting surveillance
	Hongyu Liang; Jinchen Wu; Kaiqi Huang
	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
内容类型	会议论文
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.