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Smart video sensors for applications related to surveillance and security areIOT-based as they use Internet for various purposes. Such applications includecrowd behaviour monitoring and advanced decision support systems operating andtransmitting information over internet. The analysis of crowd and pedestrianbehaviour is an important task for smart IoT cameras and in particular videoprocessing. In order to provide related behavioural models, simulation andtracking approaches have been considered in the literature. In both casesground truth is essential to train deep models and provide a meaningfulquantitative evaluation. We propose a framework for crowd simulation andautomatic data generation and annotation that supports multiple cameras andmultiple targets. The proposed approach is based on synthetically generatedhuman agents, augmented frames and compositing techniques combined with pathfinding and planning methods. A number of popular crowd and pedestrian datasets were used to validate the model, and scenarios related to annotation andsimulation were considered.

Smart IoT Cameras for Crowd Analysis based on augmentation for automatic pedestrian detection, simulation and annotation