Directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning

S. Medasani; Y. Owechko; D. Allen; T. C. Lu; D. Khosla

doi:10.1117/12.850762

26 April 2010 Directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning

S. Medasani, Y. Owechko, D. Allen, T. C. Lu, D. Khosla

Proceedings Volume 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII; 76680L (2010) https://doi.org/10.1117/12.850762
Event: SPIE Defense, Security, and Sensing, 2010, Orlando, Florida, United States

Abstract

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.

Citation Download Citation

S. Medasani, Y. Owechko, D. Allen, T. C. Lu, and D. Khosla "Directed area search using socio-biological vision algorithms and cognitive Bayesian reasoning", Proc. SPIE 7668, Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VII, 76680L (26 April 2010); https://doi.org/10.1117/12.850762

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KEYWORDS

Visualization

Visual process modeling

Particles

Buildings

Video

Video surveillance

Cognitive modeling

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