Ontology-based trajectory analysis for semantic event detection

The extraction of human centered descriptions, matching end users cognition, and specifically the detection and identification of events in videos is a particularly challenging problem, due to the volume and diversity of both the automatically extracted low-level features and the corresponding high-level information conveyed. Numerous efforts have begun, attempting to bridge the semantic gap between lowlevel data and higher level descriptions, often resorting to domain-specific learning-based approaches. In this paper we present a novel, generally applicable approach, for hierarchical semantic analysis of spatiotemporal video features (trajectories) in order to localize and detect events of interest. Dynamically changing trajectories are extracted by processing the optical flow, based on its statistics. The temporal evolution of the trajectories ’ geometrical and spatiotemporal characteristics forms the basis on which event detection is performed. This is based on the exploitation of prior knowledge, which provides the formal conceptualization needed to enable the automatic inference of high level event descriptions. Experimental results with a variety of surveillance videos are presented to exemplify the usability and effectiveness of the proposed system. 1