DocumentCode :
2452787
Title :
Biologically-inspired approaches to higher-level information fusion
Author :
Rhodes, Bradley J.
Author_Institution :
Adv. Inf. Technol., Burlington
fYear :
2007
fDate :
9-12 July 2007
Firstpage :
1
Lastpage :
3
Abstract :
Contemporary situational awareness problems such as automated normalcy learning for anomaly detection and motion behavior prediction are addressed with biologically-inspired processing, representation, and learning approaches. Issues and challenges are discussed and our responses to them described. Relatively simple neural principles provide considerable power in providing capabilities required to learn models of normal motion behavior and utilize those models to identify unusual behavior or determine the most likely future behavior of objects of interest.
Keywords :
learning (artificial intelligence); neural nets; prediction theory; sensor fusion; anomaly detection; automated normalcy learning; biologically-inspired approaches; contemporary situational awareness problems; higher-level information fusion; motion behavior prediction; neural network; situation awareness; Adaptive systems; Biological system modeling; Context modeling; Humans; Information technology; Labeling; Learning systems; Motion detection; Neural networks; Training data; Higher-level fusion; learning; neural networks; prediction; situation awareness;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Fusion, 2007 10th International Conference on
Conference_Location :
Quebec, Que.
Print_ISBN :
978-0-662-45804-3
Electronic_ISBN :
978-0-662-45804-3
Type :
conf
DOI :
10.1109/ICIF.2007.4408213
Filename :
4408213
Link To Document :
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