Human Flow Monitoring System Based on Multitask Fully Convolutional Network
WEI Rui
PENG Tianliang
Abstract：Due to the change of scale,it is a challenge to estimate the number of people in the picture.With the development of deep learning,there are some neural network models based on multi-column or multi-network to extract the scale-dependent fea?tures to enhance the density estimation However,these models are more complex in optimization training and require a huge amount of computational resources.In view of this,we propose a multitasking fully convolutional network to estimate the number of people. Based on the convolution operation of different scales,we can extract the scale-related features and estimate the population density and population at the same time,Use efficiency,and then realize the estimation of high density of people.Experiments show that the proposed model has better accuracy and higher robustness.
Keywords：multitasking full convolution neural networkestimation of population densitymonitoring of population flow
Publication Date：2018-03-02
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：4( 489-491,500 )