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Real-time Video Analysis from a Mobile Platform
Details
In this book we present a vision system for autonomous navigation of a mobile platform. The system is enable to interact with its immediate surroundings, recognizing obstacles and other moving objects, and obtaining a stable view of the world. In fact, a vision system for autonomous navigation has to detect the objects in the environment and classify them as obstacles , in order to avoid them, or as target objects, in order to follow them. We face the challenging problem of the Obstacle detection and avoidance . It consists of obstacle detection in order to find the safety path to follow during the autonomous navigation of a mobile platform. The major contribution of this work concerns a perceptive representation of the environment, that it is not a passive representation, but related to the final goal of autonomous navigation. It is based on the stereo vision paradigm and detect obstacles and moving objects in the scene right according to the autonomous navigation goal, that is obtaining a result as fine as it is enough for our aims. The results of our method for stereo vision are shown in a comparison with the best algorithms in the literature.
Autorentext
Alessandro Limongiello was born on 31 March 1976 in Atripalda (AV)-Italy, received Laurea degree in computer engineering in 2002 (University "Federico II" of Naples-Italy), Ph.D. in 2007 (University of Salerno-Italy), and Ph.D. in 2007 (INSA of Lyon-France). His research interests concern Pattern Recognition, Computer Vision, Neuroscience, Optics.
Weitere Informationen
- Allgemeine Informationen
- GTIN 09783659205781
- Sprache Englisch
- Größe H220mm x B150mm x T10mm
- Jahr 2012
- EAN 9783659205781
- Format Kartonierter Einband
- ISBN 3659205788
- Veröffentlichung 01.11.2012
- Titel Real-time Video Analysis from a Mobile Platform
- Autor Alessandro Limongiello
- Untertitel Moving Object and Obstacle Detection
- Gewicht 244g
- Herausgeber LAP LAMBERT Academic Publishing
- Anzahl Seiten 152
- Genre Informatik