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Context-based semantic labeling of human-vehicle interactions in persistent surveillance systems

The improved Situational awareness in Persistent Surveillance Systems (PSS) is an ongoing research effort of the Department of Defense. Most PSS generate huge volume of raw data and they heavily rely on human operators to interpret and inference data in order to detect potential threats. Many outdoor apprehensive activities involve vehicles as their primary source of transportation to and from the scene where a plot is executed. Vehicles are employed to bring in and take out ammunitions, supplies, and personnel. Vehicles are also used as a disguise, hide-out, a meeting place to execute threat plots. Analysis of the Human-Vehicle Interactions (HVI) helps us to identify cohesive patterns of activities representing potential threats. Identification of such patterns can significantly improve situational awareness in PSS. In our approach, image processing technique is used as the primary source of sensing modality. We use HVI taxonomy as a means for recognizing different types of HVI activities. HVI taxonomy may comprise multiple threads of ontological patterns. By spatiotemporal linking of ontological patterns, a HVI pattern is hypothesized to pursue a potential threat situation. The proposed technique generates semantic messages describing ontology of HVI. This paper also discusses a vehicle zoning technique for HVI semantic labeling and demonstrates efficiency and effectiveness of the proposed technique for identifying HVI.