Items related to A Novel Image-based Model for Data Fusion in Surveillance...
Synopsis
Surveillance systems heavily rely on data collected by multi-modality sensors to detect and characterize behavior of entities and events over a given situation. In order to transform the multi-modality sensors data into useful information leading to actionable information, there is an essential need for a robust data fusion model. A robust fusion model should be able to acquire data from multi-agent sensors and take advantage of spatio-temporal characteristics of multi-modality sensors to create a better situational awareness ability and in particular, assisting with soft fusion of multi-threaded information from variety of sensors under task uncertainties. This research presents a novel Image-based model for multi-modality data fusion. The concept of this fusion model is biologically-inspired by the human brain energy perceptual model. Similar to the human brain having designated regions to map immediate sensory experiences and fusing collective heterogeneous sensory perceptions to create a situational understanding for decision-making, the proposed image-based fusion model follows an analogous data to information fusion scheme for actionable decision-making.
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About the Author
Dr. Aaron Rasheed Rababaah is an Associate Professor of Computer Science at the American University of Kuwait. He Holds a BSc in Industrial Eng, MSc in Computer Science and PhD in Computer Systems Engineering. He has 7-year teaching experience at four universities. His research interests include: intelligent systems, machine vision and robotics.
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