Estimation Population Density Built on Multilayer Convolutional Neural Network

摘要

Automatic population density estimation is a significant study area in intelligent video monitoring. Traditional methods need design features manually, which are hard to keep pace with the current state of big data. At the same time, with the outbreak of artificial intelligence methods such as deep learning, the application of deep learning to video monitoring is also an irresistible trend. Therefore, according to the disadvantage of traditional manual feature extraction and the deficiency of single-layer convolutional neural network (CNN), a multilayer convolutional neural network (MCNN) is raised. In this article, head size changes caused by various reasons, such as penetration effect, will not affect the characteristics of CNN learning pictures. That is to say, even if we do not know the perspective of the input map, we can accurately detect the population density on the basis of adaptive kernel. The characteristic graphs of each layer are integrated to obtain the population density map. experiments reveals that this network structure can attain more accurate population estimation.