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%.