Obstacle detection in planar worlds using cellular neural networks
Research output: Contribution to conferences › Paper › Contributed › peer-review
Contributors
Abstract
Obstacle detection in planar worlds is an important part of computer vision because it is indispensable for collision prevention of autonomously navigating moving objects. For example, vehicles driving without human guidance need robust prediction of potential obstacles, like other vehicles or pedestrians. Most common approaches of obstacle detection so far have used analytical and statistical methods like motion estimation or generation of maps. The proposed procedures are mostly composed of many processing steps, so that error propagation of successive steps often leads to inaccurate results. Another problem is the necessity of high computing power for real time applications. In this contribution we demonstrate that obstacle detection in planar worlds can be performed efficiently using cellular neural networks. Beside a fast processing speed the proposed method is also very robust.
Details
Original language | English |
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Pages | 383-390 |
Number of pages | 8 |
Publication status | Published - 2002 |
Peer-reviewed | Yes |
Externally published | Yes |
Workshop
Title | 7th IEEE International Workshop on Cellular Neural Networks and their Applications |
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Abbreviated title | CNNA 2002 |
Conference number | 7 |
Duration | 22 - 24 July 2002 |
Degree of recognition | International event |
City | Frankfurt am Main |
Country | Germany |
External IDs
ORCID | /0000-0001-7436-0103/work/142240270 |
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Keywords
ASJC Scopus subject areas
Keywords
- Cellular neural networks, Computer vision, Humans, Motion detection, Navigation, Object detection, Remotely operated vehicles, Robustness, Statistical analysis, Vehicle driving