The Development of an Improvised Crowd Analysis Model

Abstract

Abstract: The goal of this project is to create a deep learning-based visual crowd counting system. The objective of this project is to build a functioning system that can analyze pictures and determine the number of individuals present within these images. It will also demonstrate its approximate density map, a graph comparing the expected count to the actual count, and information on its accuracy in terms of Mean Absolute Error (MAE). To routinely supervise audiences, researchers recently moved to computer vision. This research analysis proposed the implementation of a Deep learning algorithm CNN for which the aim was to detect the crowd and estimate increased influx of people which has been successfully achieved by employing deep learning technique. We have used the shanghai tech dataset part B for our research purpose. CNN model detected the crowd and estimated the density with an absolute error of 21. In 312 we obtained a validation mean absolute error of 21.3, which means on average, the model will estimate 21 persons in excess or deficit.