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Crowd counting oriented to indoor scenes based on FCN with inception modules and residual networks

In public places, the number of people is an important indicator of population safety, especially for indoor closed places. In this paper, we propose a new fully convolutional neural network (FCN) to calculate the indoor pop­ulation and draw a density map which can reflect crowd distribution. We add inception modules and residual network structures to our network, which greatly enhances the accuracy rate about the prediction of the population and solves the problem of overfitting. Compared with the prior art methods, our proposed scheme achieves a faster, less space- consuming and more accurate prediction of the population density.