Crowd Counting via Multi-layer Regression

Crowd counting aims to estimate the number of persons in a crowd image--a challenge until this day--as congestion degree varies, people's appearances may seem different. To address this problem, we propose a novel crowd counting method named Multi-layer Regression Network (MRNet), which consists of a multi-layer recognition branch and several density regressors. In practice, the recognition branch recognizes the congestion degree of the regions in a crowd image, then disintegrates the image into background and several crowd regions layer by layer, each regions are assigned different congestion degrees. In each layer, the recognized crowd regions with the specific congestion degree are delivered to a regressor with the corresponding density prior for crowd density estimation. The generated density maps at all layers are integrated to obtain the final density map for crowd density estimation. To date, MRNet is the first method to estimate crowd densities on crowd regions with different regressors. We conduct a comprehensive evaluation of MRNet on four typical datasets in comparison with nine state-of-the-art methods. By using multi-layer regression, MRNet achieves significant improvement in crowd counting accuracy, and outperforms the state-of-the-art methods.