27 October 2013 Statistics of the number of students in the classroom based on frequency domain analysis and seat detection
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Proceedings Volume 8919, MIPPR 2013: Pattern Recognition and Computer Vision; 89190E (2013) https://doi.org/10.1117/12.2031505
Event: Eighth International Symposium on Multispectral Image Processing and Pattern Recognition, 2013, Wuhan, China
Abstract
Statistics of the number of students in the classroom is very important for class surveillance. It can help teacher count the number of students and help students choose class for self-study. While as a canonical pattern recognition problem, it’s very difficult due to various appearances of students and other outliers such as bags and books. We want to find a good solution to this problem. A novel method for texture feature extraction is now proposed based on that difference of Frequency spectrum image belongs to different seat image. Regarding frequency spectrum image as the texture image, the texture characteristics which can represent those differences are extracted using texture analysis's method. At the same time, we combine the Local binary patterns feature with the texture characteristics to describe the texture of seats. Experiments on a real classroom dataset demonstrate that the accuracy of the proposed method reaches 91.3%.
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Xunxun Wei, Rui Liu, Wenke Zhang, Ming Zhu, "Statistics of the number of students in the classroom based on frequency domain analysis and seat detection", Proc. SPIE 8919, MIPPR 2013: Pattern Recognition and Computer Vision, 89190E (27 October 2013); doi: 10.1117/12.2031505; https://doi.org/10.1117/12.2031505
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