Survey on algorithms of people counting in dense crowd and crowd density estimation

The number of people and the estimation of the population density are one of the important information concerned by intelligent monitoring. This article reviews, summarizes, and classifies the research methods and status of population statistics and population density estimation. According to the different methods of obtaining crowd information, the research methods of population density statistics and population density estimation are divided into detection-based methods, regression-based methods, and density-based methods. The methods based on density estimation are studied in detail. The network structures based on density estimation are divided into a single-column deep convolutional neural network and a multi-column convolutional neural network. Summarized the basic ideas, advantages and disadvantages of each method, analyzed and introduced the representative algorithms of each method, analyzed and compared related experiments, and introduced common mainstream data sets and performance evaluations for population statistics and population density estimation. Indicators and evaluation methods, and prospects for future possible research directions and corresponding development trend in this field.