Learning how to analyse crowd behaviour using synthetic data
Anish Khadka, Mahdi Maktabdar Oghaz, Walter Matta, Marco Cosentino, Paolo Remagnino, Vasileios Argyriou · 2019
Deep learning has greatly improved pattern recognition algorithms, however its strength is also its weakness: training data must be plentiful, varied and, above all, annotated. Annotating data for crowd analysis in unrealistic: for instance, consider the task of accurately counting people in a dense crowd. Such task would require hours of painstaking manual marking where people are in a crowd that might have been captured from far field. We propose a method that builds controlled simulations of people moving in a real environment; people are modelled as synthetic humanoids with realistic appearance, while the background is the actual image of a scene. Controlling illumination, dynamics of people and density in the scene, we can virtually generate an infinite number of simulations and very large annotated datasets. This paper demonstrates how the performance of a conventional deep network, used for a crowd analysis task, can be improved by using simulated datasets.