MFNet: Multi-feature convolutional neural network for high-density crowd counting

Abstract

The crowd counting task involves the issue of security, so now more and more people are concerned about it. At present, the most difficult problem of population counting consists in: how to make the model distinguish human head features more finely in the densely populated area, such as head overlap and how to find a small-scale local head feature in an image with a wide range of population density. Facing these challenges, we propose a network for multiple feature convolutional neural network, which is called MFNet. It aims to get high-quality density maps in the high-density crowd scene, and at the same time to perform the task of the count and estimation of the crowd. In terms of crowd counting, we use multiple sources of information, that is HOG, LBP and CANNY. With the support vector machine (SVM), each source provides us not merely a separate count estimation, but other statistical measures. In order to effectively solve the problem of extracting scale-related features in crowd counting, we have integrated MFNet, a convolutional neural network architecture. By comparing the experimental results of multiple data sets, MFNet is superior to other population counting methods.