Machine Learning for Dense Crowd Direction Prediction Using Long Short-Term Memory
Abdullah A Alajlan, Alaa Edris, Frederick T. Sheldon, Terence Soule · 2020
The safety of a dense crowd is one of the most important matters for an event’s organizers. Therefore, management of the crowd, and noticing any serious issues in advance becomes important. Developing a crowd simulation by using a social force model simulates the behavior of crowds in reality. The prediction of individual agents’ behavior in the simulation and how the agents interact with each other can improve the safety of dense crowds. Depending on the success of Recurrent Neural Network(RNN) handling of sequential data, we propose a model that is based on Long Short Term Memory (LSTM) to predict individual agents’ next locations. Our proposed approach will be tested two different densities of crowds, structured crowds, and unstructured crowds. In structured crowds, people generally move in one direction and head to the same destination, such as at the Islamic Hajj. In unstructured crowds people move in many different directions and head for different destinations, such as in public town squares.