DocumentCode :
2375541
Title :
Iterative Learning and Self-Optimization Techniques for the Innovative Railcab-System
Author :
Trachtler, Ansgar ; Munch, E. ; Vocking, Henner
Author_Institution :
Institute of Mechatronics & Design Eng., Paderborn
fYear :
2006
fDate :
6-10 Nov. 2006
Firstpage :
4683
Lastpage :
4688
Abstract :
In this paper, we propose a new concept for information processing of networked vision sensors for surveillance. The networked sensor technology has a potential capability to solve some of our most important scientific and societal problems. But, difficulties of processing are always big problems in case of such huge amount of information acquired by the distributed vision systems. The proposed concept gets a hint from information processing of human hearing organs and compound eyes of insects. By a basic experiment, we confirmed that the proposed concept can be utilized to detect human behavior
Keywords :
distributed sensors; image sensors; iterative methods; learning (artificial intelligence); railways; surveillance; distributed vision systems; information processing; iterative learning; networked sensor technology; railcab-system; self-optimization techniques; surveillance vision sensors; Communication system control; Design engineering; Hardware; Information processing; Mechatronics; Mobile robots; Navigation; Propulsion; Remotely operated vehicles; Vehicle driving;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
IEEE Industrial Electronics, IECON 2006 - 32nd Annual Conference on
Conference_Location :
Paris
ISSN :
1553-572X
Print_ISBN :
1-4244-0390-1
Type :
conf
DOI :
10.1109/IECON.2006.347957
Filename :
4153578
Link To Document :
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