  Smart video sensors for applications related to surveillance and security are
IOT-based as they use Internet for various purposes. Such applications include
crowd behaviour monitoring and advanced decision support systems operating and
transmitting information over internet. The analysis of crowd and pedestrian
behaviour is an important task for smart IoT cameras and in particular video
processing. In order to provide related behavioural models, simulation and
tracking approaches have been considered in the literature. In both cases
ground truth is essential to train deep models and provide a meaningful
quantitative evaluation. We propose a framework for crowd simulation and
automatic data generation and annotation that supports multiple cameras and
multiple targets. The proposed approach is based on synthetically generated
human agents, augmented frames and compositing techniques combined with path
finding and planning methods. A number of popular crowd and pedestrian data
sets were used to validate the model, and scenarios related to annotation and
simulation were considered.
