In everyday life as well as in asymmetric warfare domain, to achieve the intended goals, agents often do not make use of the designed and purpose-built tools, but some other tools whose features simply fit for the purpose. The present paper discusses the possibility of capturing and integrating relations and features from context that could drive the retrieval of possible candidate substitutes for the properly designed artifacts through Lexical Semantics Exploitation. Generative Lexicon theory assumes a structure (QualiaStructure) organizing the semantic content carried by lexical items through roles. Among them, theTelicrole exposes the function or purpose of the predicated entity and theConstitutiverole exposes its component parts. We argue that the typical function an entity has been thought for is related to its internal constituents. We also argue that a knowledge base and a proper metrics can be conveniently built extractingQualiaelements from suitable text corpora.

Tracking objects in complex dynamic environments can be less challenging once their behavior is recognized. Inferring on targets' future actions based on their past can be addressed via probabilistic reasoning. Context information plays a crucial role in the reasoning process as it provides additional clues about targets' behavior. Combining context reasoning with target tracking continues to increase with the availability of supporting information. The framework here discussed views target's actions as a Hidden Markov Model (HMM) with relevant context associated with each node. Context is at each time step selected based on immediate and goal driven sets of actions. Inference in the HMM is conditioned on prior target's measurements and the belief state conditioned on context. This posterior is then compared with the target's state estimate in order to adjust the switching probability in the Interactive Multiple Models (IMM) tracking process.

In this paper, we address the problem of representing domain knowledge for situation awareness in a security application. While ontologies are appropriate for describing taxonomical knowledge, they cannot express more complex knowledge such as entailments. In this paper, we describe how domain knowledge can be encoded through OWL ontologies and SWRL rules in order to reason about the entities and their interactions in a surveillance application. We describe how events can be described through ontologies and how video sequences can be annotated using the MPEG-7 standard.

In this paper, we address the problem of identifying anomalous events in the context of a multi sensor surveillance system. Targets' trajectories are analyzed and compared to common patterns of activity represented as clusters of trajectories. Here we extend our previous work to cater for observations provided by multiple cameras observing the same scene. Data fusion is performed within the Dempster-Shafer theory of evidence framework. The proposed approach is validated through experimental results performed in the context of an automatic road traffic monitoring application