The evaluation of a country's critical infrastructure requires a detailed analysis of facilities such as airfields, harbors and
heavy industry. To improve the assessment of such facilities, an assistance system for the interpretation of infrastructure
facilities from aerial imagery is developed. In this paper we point out recent advances of the system's recommendation
function. Besides suggesting the occurrence of undetected objects based on a probabilistic scene model and previously
detected objects, the system is now able to suggest the classification of objects based on intrinsic object features and both
local context (spatial relations) and global context (overall scene classification). To justify our approach the results of an
experimental evaluation of the system for the classification of industrial installations is presented.
Object recognition is a typical task of aerial reconnaissance and especially in military applications, to determine the class
of an unknown object on the battlefield can give valuable information on its capabilities and its threat. RecceMan®
(Reconnaissance Manual) is a decision support system for object recognition developed by the Fraunhofer IOSB. It
supports object recognition by automating the tedious task of matching the object features with the set of possible object
classes, while leaving the assessment of features to the trained human interpreter. The quality of the features assessed by
the user is influenced by several factors such as the quality of the image of the object. These factors are potential sources
of error, which can lead to an incorrect classification and therefore have to be considered by the system. To address this
issue, two methods for consideration of uncertainty in human feature assessment - a probabilistic and a heuristic
approach - are presented and compared based on an experiment in the exemplary domain of flower recognition.
KEYWORDS: Video surveillance, Sensors, Surveillance, Received signal strength, Video, Signal processing, Information security, Computing systems, Surveillance systems, Data fusion
From the advances in computer vision methods for the detection, tracking and recognition of objects in video streams,
new opportunities for video surveillance arise: In the future, automated video surveillance systems will be able to detect
critical situations early enough to enable an operator to take preventive actions, instead of using video material merely
for forensic investigations. However, problems such as limited computational resources, privacy regulations and a
constant change in potential threads have to be addressed by a practical automated video surveillance system. In this
paper, we show how these problems can be addressed using a task-oriented approach. The system architecture of the
task-oriented video surveillance system NEST and an algorithm for the detection of abnormal behavior as part of the
system are presented and illustrated for the surveillance of guests inside a video-monitored building.
KEYWORDS: Visual process modeling, Bayesian inference, Machine vision, Computer vision technology, Computing systems, Statistical analysis, Remote sensing, Data modeling, Decision support systems, Monte Carlo methods
The interpretation of complex scenes requires a large amount of prior knowledge and experience. To utilize prior
knowledge in a computer vision or a decision support system for image interpretation, a probabilistic scene model for
complex scenes is developed. In conjunction with a model of the observer's characteristics (a human interpreter or a
computer vision system), it is possible to support bottom-up inference from observations to interpretation as well as to
focus the attention of the observer on the most promising classes of objects. The presented Bayesian approach allows
rigorous formulation of uncertainty in the models and permits manifold inferences, such as the reasoning on unobserved
object occurrences in the scene. Monte-Carlo methods for approximation of expectations from the posterior distribution
are presented, permitting the efficient application even for high-dimensional models. The approach is illustrated on the
interpretation of airfield scenes.
The evaluation of a country's critical infrastructure requires a detailed analysis of facilities such as airfields, harbors,
communication lines and heavy industry. To improve the interpretation process, an interactive support system for the
interpretation of infrastructure facilities from aerial imagery is developed. The aim is to facilitate the training phase for
beginners, increase the flexibility in the assignment of interpreters and improve the overall quality of the interpretation.
An analysis of the approach chosen by professional interpreters has been the basis to identify critical steps which can be
effectively supported by a software system. To evaluate the benefit of the system, an experimental setup is proposed.
The analysis of complex infrastructure from aerial imagery, for instance a detailed analysis of an airfield, requires the
interpreter, besides to be familiar with the sensor's imaging characteristics, to have a detailed understanding of the
infrastructure domain. The required domain knowledge includes knowledge about the processes and functions involved
in the operation of the infrastructure, the potential objects used to provide those functions and their spatial and functional
interrelations. Since it is not possible yet to provide reliable automatic object recognition (AOR) for the analysis of such
complex scenes, we developed systems to support a human interpreter with either interactive approaches, able to assist
the interpreter with previously acquired expert knowledge about the domain in question, or AOR methods, capable of
detecting, recognizing or analyzing certain classes of objects for certain sensors. We believe, to achieve an optimal result
at the end of an interpretation process in terms of efficiency and effectivity, it is essential to integrate both interactive and
automatic approaches to image interpretation. In this paper we present an approach inspired by the advancing semantic
web technology to represent domain knowledge, the capabilities of available AOR modules and the image parameters in
an explicit way. This enables us to seamlessly extend an interactive image interpretation environment with AOR
modules in a way that we can automatically select suitable AOR methods for the current subtask, focus them on an
appropriate area of interest and reintegrate their results into the environment.
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