Crowd Behaviour Understanding Using Computer Vision and Statistical Mechanics Principles
Crowd behaviour understanding in computer science is a research discipline which has grown rapidly in recent years. Specifically, we are currently able to generate large and high-resolution observation data through crowd sensing in varieties of spatial environments. This has also given us the advantage to adopt computer vision methods for detecting human behaviour. In this study, we adopted statistical mechanics principles with analogies of entropy and kinetic energy in classical molecular gases to derive features which describe crowd motions. These are implicitly measured, as basis for understanding behaviour, using a holistic three-dimensional representation, of crowd features including structure, energy and translation. As a result, we measured those features using computer vision in the view of machine understanding crowd behaviour. Usual behaviour is established from our expected crowd motions in context of the specific recipient spaces of our experiments. The behaviour which does not fall within the expected usual behaviour measurement is considered as an unusual behaviour. This research work was initiated in 2013 under the eVACUATE project, while it is currently ongoing under the S4AllCities project since 2020.