Qualitative Motion Understanding:
Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade,...
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| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Boston, MA
Springer US
1992
|
| Series: | The Springer International Series in Engineering and Computer Science, Robotics: Vision, Manipulation and Sensors
184 |
| Subjects: | |
| Online Access: | BTU01 URL des Erstveröffentlichers |
| Summary: | Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade, searching for solutions to some of these problems; however, it still remains one of the more difficult and challenging areas of computer vision research. Qualitative Motion Understanding describes a qualitative approach to dynamic scene and motion analysis, called DRIVE (Dynamic Reasoning from Integrated Visual Evidence). The DRIVE system addresses the problems of (a) estimating the robot's egomotion, (b) reconstructing the observed 3-D scene structure; and (c) evaluating the motion of individual objects from a sequence of monocular images. The approach is based on the FOE (focus of expansion) concept, but it takes a somewhat unconventional route. The DRIVE system uses a qualitative scene model and a fuzzy focus of expansion to estimate robot motion from visual cues, to detect and track moving objects, and to construct and maintain a global dynamic reference model |
| Physical Description: | 1 Online-Ressource (XIII, 210 p) |
| ISBN: | 9781461535669 |
| DOI: | 10.1007/978-1-4615-3566-9 |
Staff View
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| 520 | |a Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade, searching for solutions to some of these problems; however, it still remains one of the more difficult and challenging areas of computer vision research. Qualitative Motion Understanding describes a qualitative approach to dynamic scene and motion analysis, called DRIVE (Dynamic Reasoning from Integrated Visual Evidence). The DRIVE system addresses the problems of (a) estimating the robot's egomotion, (b) reconstructing the observed 3-D scene structure; and (c) evaluating the motion of individual objects from a sequence of monocular images. The approach is based on the FOE (focus of expansion) concept, but it takes a somewhat unconventional route. The DRIVE system uses a qualitative scene model and a fuzzy focus of expansion to estimate robot motion from visual cues, to detect and track moving objects, and to construct and maintain a global dynamic reference model | ||
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Record in the Search Index
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| discipline | Informatik |
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| id | DE-604.BV045187015 |
| illustrated | Not Illustrated |
| indexdate | 2024-07-10T08:10:58Z |
| institution | BVB |
| isbn | 9781461535669 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-030576192 |
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| physical | 1 Online-Ressource (XIII, 210 p) |
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| publishDate | 1992 |
| publishDateSearch | 1992 |
| publishDateSort | 1992 |
| publisher | Springer US |
| record_format | marc |
| series2 | The Springer International Series in Engineering and Computer Science, Robotics: Vision, Manipulation and Sensors |
| spelling | Burger, Wilhelm Verfasser aut Qualitative Motion Understanding by Wilhelm Burger, Bir Bhanu Boston, MA Springer US 1992 1 Online-Ressource (XIII, 210 p) txt rdacontent c rdamedia cr rdacarrier The Springer International Series in Engineering and Computer Science, Robotics: Vision, Manipulation and Sensors 184 Mobile robots operating in real-world, outdoor scenarios depend on dynamic scene understanding for detecting and avoiding obstacles, recognizing landmarks, acquiring models, and for detecting and tracking moving objects. Motion understanding has been an active research effort for more than a decade, searching for solutions to some of these problems; however, it still remains one of the more difficult and challenging areas of computer vision research. Qualitative Motion Understanding describes a qualitative approach to dynamic scene and motion analysis, called DRIVE (Dynamic Reasoning from Integrated Visual Evidence). The DRIVE system addresses the problems of (a) estimating the robot's egomotion, (b) reconstructing the observed 3-D scene structure; and (c) evaluating the motion of individual objects from a sequence of monocular images. The approach is based on the FOE (focus of expansion) concept, but it takes a somewhat unconventional route. The DRIVE system uses a qualitative scene model and a fuzzy focus of expansion to estimate robot motion from visual cues, to detect and track moving objects, and to construct and maintain a global dynamic reference model Computer Science Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Image Processing and Computer Vision Computer science Artificial intelligence Computer graphics Image processing Control engineering Robotics Mechatronics Bhanu, Bir aut Erscheint auch als Druck-Ausgabe 9781461365846 https://doi.org/10.1007/978-1-4615-3566-9 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Burger, Wilhelm Bhanu, Bir Qualitative Motion Understanding Computer Science Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Image Processing and Computer Vision Computer science Artificial intelligence Computer graphics Image processing Control engineering Robotics Mechatronics |
| title | Qualitative Motion Understanding |
| title_auth | Qualitative Motion Understanding |
| title_exact_search | Qualitative Motion Understanding |
| title_full | Qualitative Motion Understanding by Wilhelm Burger, Bir Bhanu |
| title_fullStr | Qualitative Motion Understanding by Wilhelm Burger, Bir Bhanu |
| title_full_unstemmed | Qualitative Motion Understanding by Wilhelm Burger, Bir Bhanu |
| title_short | Qualitative Motion Understanding |
| title_sort | qualitative motion understanding |
| topic | Computer Science Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Image Processing and Computer Vision Computer science Artificial intelligence Computer graphics Image processing Control engineering Robotics Mechatronics |
| topic_facet | Computer Science Computer Imaging, Vision, Pattern Recognition and Graphics Artificial Intelligence (incl. Robotics) Control, Robotics, Mechatronics Image Processing and Computer Vision Computer science Artificial intelligence Computer graphics Image processing Control engineering Robotics Mechatronics |
| url | https://doi.org/10.1007/978-1-4615-3566-9 |
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