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2014
Conference Paper
Titel
Architecture for and evaluation of situational analysis in the real world
Abstract
Automatic situational analysis in video surveillance enables smart and exhaustive observation of what is happening in a scene. In real world applications there arise special challenges concerning imperfect input data and real-time processing requirements. In this paper a hierarchical and distributed architecture tackling situation analysis in the real world is introduced, described, and exemplarily evaluated. The architecture introduced is hierarchically and modularly structured. The low-level data acquisition and processing is encapsulated in self-contained building blocks. These modules include video acquisition, sensor feedback and control, person detection and tracking, and vehicle identification. High level situation analysis is provided with a knowledge based approach using Fuzzy Metric Temporal Logic and Situation Graph Trees. The communication between the modules is realized with a persistence layer serving simultaneously as the knowledge base for the inference engine. Finally, the overall performance of recognizing situations is evaluated.