Crowd Density Estimation using Novel Feature Descriptor
This work addresses crowd monitoring for public safety, but it is incremental as it builds on existing texture descriptors and datasets.
The paper tackles crowd density estimation by proposing a new texture feature-based approach using Completed Local Binary Pattern (CLBP) and a multi-class SVM classifier, achieving 95% accuracy on the PETS 2009 dataset and outperforming other state-of-the-art methods.
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.