Crowd Counting Network Based on Feature Enhancement Loss and Foreground Attention

Abstract

Crowd counting aims to estimate the total number of people in an image and present its distribution accurately.The images in the relevant datasets usually involve a variety of scenes and include multiple people.To save labor,most datasets usually annotated each human head by a single point.However,the point labels cannot cover the full human head,which makes it difficult to converge the matching between the crowd feature and the distribution label,and the predicted values cannot be gathered in the foreground region,which seriously affects the density estimation map quality and count accuracy.To solve this problem,count loss is used to constrain the range of predictions on the full map,and a pixel-level distribution consistency loss is used to optimize the density map matching process.In addition,there are many background noises that are easily confused with crowd feature in complex scenes.In order to avoid the interference of false positive predictions on subsequent counting and density map estimation,a foreground segmentation module and feature enhancement loss are proposed to adaptively focus the foreground region and increase the contribution of human head features to the counts,so as to suppress background misjudgments.In addition,in order to make the network adapt to the multi-scale pattern of the human head better,up and down sampling operations are performed on each image to be trained to obtain the multi-scale pattern with the same object.Experiments on several datasets show that the proposed method achieves better or competitive results compared with state-of-the-art methods.

Keywords

• crowd counting|deep learning|foreground segmentation|background compensation|density estimation