Proceedings Article | 26 April 2010
KEYWORDS: Visualization, Visual process modeling, Particles, Buildings, Video surveillance, Video, Cognitive modeling, Biological research, Target detection, Object recognition
Volitional search systems that assist the analyst by searching for specific targets or objects such as vehicles, factories,
airports, etc in wide area overhead imagery need to overcome multiple problems present in current manual and automatic
approaches. These problems include finding targets hidden in terabytes of information, relatively few pixels on targets,
long intervals between interesting regions, time consuming analysis requiring many analysts, no a priori representative
examples or templates of interest, detecting multiple classes of objects, and the need for very high detection rates and
very low false alarm rates.
This paper describes a conceptual analyst-centric framework that utilizes existing technology modules to search and
locate occurrences of targets of interest (e.g., buildings, mobile targets of military significance, factories, nuclear plants,
etc.), from video imagery of large areas. Our framework takes simple queries from the analyst and finds the queried
targets with relatively minimum interaction from the analyst. It uses a hybrid approach that combines biologically
inspired bottom up attention, socio-biologically inspired object recognition for volitionally recognizing targets, and
hierarchical Bayesian networks for modeling and representing the domain knowledge. This approach has the benefits of
high accuracy, low false alarm rate and can handle both low-level visual information and high-level domain knowledge
in a single framework. Such a system would be of immense help for search and rescue efforts, intelligence gathering,
change detection systems, and other surveillance systems.
