  Crowd density estimation is an important task for crowd monitoring. Many
efforts have been done to automate the process of estimating crowd density from
images and videos. Despite series of efforts, it remains a challenging task. In
this paper, we proposes a new texture feature-based approach for the estimation
of crowd density based on Completed Local Binary Pattern (CLBP). We first
divide the image into blocks and then re-divide the blocks into cells. For each
cell, we compute CLBP and then concatenate them to describe the texture of the
corresponding block. We then train a multi-class Support Vector Machine (SVM)
classifier, which classifies each block of image into one of four categories,
i.e. Very Low, Low, Medium, and High. We evaluate our technique on the PETS
2009 dataset, and from the experiments, we show to achieve 95% accuracy for the
proposed descriptor. We also compare other state-of-the-art texture descriptors
and from the experimental results, we show that our proposed method outperforms
other state-of-the-art methods.
