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People in seats counting via seat detection for meeting surveillance
Hongyu Liang; Jinchen Wu; Kaiqi Huang
Conference NameChinese Conference on Pattern Recognition
Source PublicationChinese Conference on Pattern Recognition
Conference Date2012
Conference PlaceChina
AbstractPeople 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%.
KeywordPeople In Seats countIng   meeting Surveillance   coarse-to-fine Classification
Document Type会议论文
Corresponding AuthorKaiqi Huang
Recommended Citation
GB/T 7714
Hongyu Liang,Jinchen Wu,Kaiqi Huang. People in seats counting via seat detection for meeting surveillance[C],2012:202–210.
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