Crowd analysis and simulation using deep learning and big data analysis

This project is to look into the fundamental crowd motions in different environments including indoor/outdoor scenarios. The goal of this project is to propose a series of deep learning models and mathematical frameworks to capture the crowd motions for the purposes of analysis and simulation. The research falls into the category of data-driven crowd analysis where data is intensively used for analysis as compared to traditional empirical modelling where concise mathematical models are made trying to capture the complex structure in the data. However, because of the high complexity, more data (big data) is needed and meanwhile corresponding algorithms are to be developed. Deep learning has become a major force in many areas but not yet in this one, thus has a huge potential.