Abstract
Crowd counting and forecasting is an important problem amidst Covid 19 circumstances. A unified system to automate crowd monitoring, collect data about crowdedness and predict future crowds is presented in this paper. An evaluation of existing state-of-the-art crowd counting algorithms on a novel dataset is conducted in the first part of the paper, which demonstrates the shortcomings of these algorithms. Several novel algorithms, including a densely connected neural network, convolutional neural network, and a long short term memory based recurrent neural network, for predicting crowd counts in the near and distant future are presented afterwards in the second half of the paper.
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