Cellular neural networks for motion estimation and obstacle detection

Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a ro
Obstacle detection is an important part of Video Processing because it is indispensable for a collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need a robust prediction of potential obstacles, like other vehicles or pedestrians. Most of the common approaches of obstacle detection so far use analytical and statistical methods like motion estimation or generation of maps. In the first part of this contribution a statistical algorithm for obstacle detection in monocular video sequences is presented. The proposed procedure is based on a motion estimation and a planar world model which is appropriate to traffic scenes. The different processing steps of the statistical procedure are a feature extraction, a subsequent displacement vector estimation and a robust estimation of the motion parameters. Since the proposed procedure is composed of several processing steps, the error propagation of the successive steps often leads to inaccurate results. In the second part of this contribution it is demonstrated, that the above mentioned problems can be efficiently overcome by using Cellular Neural Networks (CNN). It will be shown, that a direct obstacle detection algorithm can be easily performed, based only on CNN processing of the input images. Beside the enormous computing power of programmable CNN based devices, the proposed method is also very robust in comparison to the statistical method, because is shows much less sensibility to noisy inputs. Using the proposed approach of obstacle detection in planar worlds, a real time processing of large input images has been made possible.
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Metadaten
Author:Dirk Feiden, Ronald Tetzlaff
URN:urn:nbn:de:hebis:30-37651
URL:http://www.adv-radio-sci.net/1/143/2003/
DOI:http://dx.doi.org/doi:10.5194/ars-1-143-2003
ISSN:1684-9973
Parent Title (English):Advances in radio science
Publisher:Darmstadt
Place of publication:International Union of Radio Science / Landesausschuss in der Bundesrepublik Deutschland
Document Type:Article
Language:English
Date of Publication (online):2007/02/21
Year of first Publication:2003
Publishing Institution:Univ.-Bibliothek Frankfurt am Main
Release Date:2007/02/21
Volume:1
Pagenumber:5
First Page:143
Last Page:147
Note:
© 2003 Author(s). This work is licensed under a Creative Commons License. http://creativecommons.org/licenses/by-nc-sa/2.0/deed.en
Source:Adv. Radio Sci., 1, 143-147, 2003, www.adv-radio-sci.net/1/143/2003/
HeBIS PPN:195559398
Institutes:Physik
Dewey Decimal Classification:530 Physik
Sammlungen:Universitätspublikationen
Licence (German):License LogoCreative Commons - Namensnennung-Keine kommerzielle Nutzung-Weitergabe unter gleichen Bedingungen

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