A human-like description of scene events for a proper UAV-based video content analysis

In Video Surveillance age, the monitoring activity, especially from unmanned vehicles, needs some degree of autonomy in the scenario interpretation. Video Analysis tasks are crucial for the target tracking and recognition; anyway, it would be desirable if a further level of understanding could provide a comprehensive, high-level scene description, by reflecting that human cognitive capability of providing a concise scene description that comes from the analysis of involved objects relationships and actions. This paper presents a smart system to identify mobile scene objects, such as people, vehicles, automatically, by analyzing the videos acquired by drones in flight, along with the activities they carried out, so as to depict what it happens in the scene from a high-level perspective. The system uses Artificial Vision methods to detect and track the mobile objects and the area where they move, and Semantic Web technologies to provide a high-level description of the scenario. Spatio/temporal relations among the tracked objects as well as simple object activities (events) are described. By semantic reasoning, the system is able to connect the simple activities into more complex activities, that better reflect a human-like description of a scenario portion. Tests conducted on several videos, showing scenarios set in different environments, return convincing results which affirm the effectiveness of the proposed approach.