Crowd counting: an overview

Crowd counting is a computer vision task that involves using computer algorithms to analyze the number and distribution of crowds. With the world's population increasing, stampedes caused by too many people continue to occur frequently, and the number of researchers paying attention to the task of crowd counting has been growing steadily. This article first introduces about the traditional crowd counting algorithm and the deep learning-driven crowd counting algorithm. It introduces two performance indicators used to assess the crowd counting algorithm, the mean absolute error (MAE, which can be represented by E_MAE) and the root mean square error (RMSE, which can be represented by E_RMSE). Finally, discussed some public datasets about crowd counting work .These datasets make much contributions to the development of algorithms, and comparing the experimental results of different algorithms can help improve crowd counting algorithms.