Improving an RCC-Derived Geospatial Approximation by OWL Axioms

Grütter, Rolf; Scharrenbach, Thomas; Bauer-Messmer, Bettina

doi:10.1007/978-3-540-88564-1_19

Cited by 19 publications

(18 citation statements)

References 9 publications

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“…RCC is an axiomatization in first order logic of certain spatial concepts and relations: partially overlaps (po), proper part (pp), inverse of proper part (ppi), and so forth. RCC semantics cannot be fully represented with ontologies [50], but typical reasoning engines provide support for them through an extended processing layer. Additional abstract spatial relations are defined inside the scene model; e.g., close to (between vessels or vessels and facilities) and aligned to (between vessels and channel navigation direction).…”

Section: Representation Of Vessel Characteristics and Harbor Areasmentioning

confidence: 99%

Context-based multi-level information fusion for harbor surveillance

et al. 2015

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Abstract:Harbor surveillance is a critical and challenging part of maritime security procedures. Building a surveil-lance picture to support decision makers in detection of potential threats requires the integration of data and information coming from heterogeneous sources. Context plays a key role in achieving this task by providing expectations, constraints and additional information for inference about the items of interest. This paper proposes a fusion system for context-based situation and threat assessment with application to harbor surveillance. The architecture of the system is organized in two levels. The lowest level uses an ontological model to formally represent input data and to classify harbor objects and basic situations by deductive reasoning according to the harbor regulations. The higher level applies Belief-based Argumen-tation to evaluate the threat posed by suspicious vessels. The functioning of the system is illustrated with several examples that reproduce common harbor scenarios. IntroductionMaritime security is an area of strategic importance for the international community. As stated in [1], ''a terrorist incident against a marine transportation system would have a disaster impact on global shipping, international trade, and the world economy in addition to the strategic military value of many ports and waterways''. For that reason, one of the principal goals of strengthening maritime security is to ''increase maritime domain awareness'' by building a ''surveillance picture as complete as possible to assess the threats and vulnerabilities in the maritime realm''. In particular, harbor surveillance is a critical part of maritime security procedures because of its multiple objectives: recognition of terrorist threats, prevention of maritime and ecological accidents, detection of illegal immigration, fishing and drug trafficking, and so forth. However, it is nowadays mostly developed by human operators [2], who have to evaluate an overwhelming amount of information. This makes it very difficult to keep track of the event stream with the required level of attention due to distraction, fatigue and oversight. In addition, their decisions may be strongly affected by sensor data imprecision and subjective judgment. make informed decisions. According to the JDL data fusion model, the latter task belongs to the domain of Situation Assessment, defined as the estimation of ''sets of relationships among entities and their implications for the states of the related entities '' [4]. In this domain, it requires understanding the intrinsic information provided by coastal sensors in the context determined by extrinsic factors, like harbor environment, operational regulations, traffic data and intelligence reports.Recently, the increasing interest in higher-level information fusion has led to several proposals for context management -see for example the special sessions on context-based information fusion celebrated in the International Conferences on Information Fusion since 2007. Detection and characteri...

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Section: Representation Of Vessel Characteristics and Harbor Areasmentioning

confidence: 99%

Context-based multi-level information fusion for harbor surveillance

et al. 2015

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“…RCC (Region Connection Calculus) is a logic-based formalism to symbolically represent and reason with topological relations between objects (Randell, Cui, & Cohn, 1992). RCC semantics cannot be directly represented with OWL (Katz & Cuenca Grau, 2005;Grüntter, Scharrenbach, & Bauer-Messmer, 2008), but RACER offers ad hoc support for them. Specifically, RACER supports the RCC-8 subset through the definition of an additional layer (substratum) and the use of special query symbols.…”

Section: Spatial and Temporal Reasoningmentioning

confidence: 99%

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Gómez‐Romero

Patricio

García

2011

Expert Systems with Applications

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a b s t r a c tComputer vision research has been traditionally focused on the development of quantitative techniques to calculate the properties and relations of the entities appearing in a video sequence. Most object tracking methods are based on statistical methods, which often result inadequate to process complex scenarios. Recently, new techniques based on the exploitation of contextual information have been proposed to overcome the problems that these classical approaches do not solve. The present paper is a contribution in this direction: we propose a Computer Vision framework aimed at the construction of a symbolic model of the scene by integrating tracking data and contextual information. The scene model, represented with formal ontologies, supports the execution of reasoning procedures in order to: (i) obtain a high-level interpretation of the scenario; (ii) provide feedback to the low-level tracking procedure to improve its accuracy and performance. The paper describes the layered architecture of the framework and the structure of the knowledge model, which have been designed in compliance with the JDL model for Information Fusion. We also explain how deductive and abductive reasoning is performed within the model to accomplish scene interpretation and tracking improvement. To show the advantages of our approach, we develop an example of the use of the framework in a video-surveillance application.

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“…Ontologies do not directly support spatial reasoning, which has given rise to the development of joint approaches that incorporate suitable additional constructors [42,43]. One of the most used is the Region Connection Calculus (RCC), a logic theory for qualitative spatial knowledge representation and reasoning [44,45].…”

Section: Spatial Reasoningmentioning

confidence: 99%

Context-based scene recognition from visual data in smart homes: an Information Fusion approach

Gómez‐Romero

Serrano

Patricio

et al. 2011

Pers Ubiquit Comput

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Ambient Intelligence (AmI) aims at the development of computational systems that process data acquired by sensors embedded in the environment to support users in everyday tasks. Visual sensors, however, have been scarcely used in this kind of applications, even though they provide very valuable information about scene objects: position, speed, color, texture, etc. In this paper, we propose a cognitive framework for the implementation of AmI applications based on visual sensor networks. The framework, inspired by the Information Fusion paradigm, combines a priori context knowledge represented with ontologies with real time single camera data to support logic-based high-level local interpretation of the current situation. In addition, the system is able to automatically generate feedback recommendations to adjust data acquisition procedures. Information about recognized situations is eventually collected by a central node to obtain an overall description of the scene and consequently trigger AmI services. We show the extensible and adaptable nature of the approach with a prototype system in a smart home scenario.

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Improving an RCC-Derived Geospatial Approximation by OWL Axioms

Cited by 19 publications

References 9 publications

Context-based multi-level information fusion for harbor surveillance

Context-based multi-level information fusion for harbor surveillance

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

Context-based scene recognition from visual data in smart homes: an Information Fusion approach

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