A Novel Image-based Model for Data Fusion in Surveillance Systems
(Sprache: Englisch)
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...
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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 perceptionsto 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.
Bibliographische Angaben
- Autor: Aaron Rababaah
- 2016, 240 Seiten, Maße: 22 cm, Kartoniert (TB), Englisch
- Verlag: LAP Lambert Academic Publishing
- ISBN-10: 3659938629
- ISBN-13: 9783659938627
Sprache:
Englisch
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