| Home | << 1 >> |
|
| Records | |||||
|---|---|---|---|---|---|
| Author | Diego Cheda; Daniel Ponsa; Antonio Lopez | ||||
| Title | Monocular Egomotion Estimation based on Image Matching | Type | Conference Article | ||
| Year | 2012 | Publication | 1st International Conference on Pattern Recognition Applications and Methods | Abbreviated Journal | |
| Volume | Issue | Pages | 425-430 | ||
| Keywords | SLAM | ||||
| Abstract | |||||
| Address | Portugal | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication  |
Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | ICPRAM | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CPL2012a;; ADAS @ adas @ | Serial | 2011 | ||
| Permanent link to this record | |||||
| Author | Diego Cheda; Daniel Ponsa; Antonio Lopez | ||||
| Title | Monocular Depth-based Background Estimation | Type | Conference Article | ||
| Year | 2012 | Publication | 7th International Conference on Computer Vision Theory and Applications | Abbreviated Journal | |
| Volume | Issue | Pages | 323-328 | ||
| Keywords | |||||
| Abstract | In this paper, we address the problem of reconstructing the background of a scene from a video sequence with occluding objects. The images are taken by hand-held cameras. Our method composes the background by selecting the appropriate pixels from previously aligned input images. To do that, we minimize a cost function that penalizes the deviations from the following assumptions: background represents objects whose distance to the camera is maximal, and background objects are stationary. Distance information is roughly obtained by a supervised learning approach that allows us to distinguish between close and distant image regions. Moving foreground objects are filtered out by using stationariness and motion boundary constancy measurements. The cost function is minimized by a graph cuts method. We demonstrate the applicability of our approach to recover an occlusion-free background in a set of sequences. | ||||
| Address | Roma | ||||
| Corporate Author | Thesis | ||||
| Publisher | Place of Publication |
Editor | |||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | ISBN | Medium | |||
| Area | Expedition | Conference | VISAPP | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CPL2012b; ADAS @ adas @ cpl2012e | Serial | 2012 | ||
| Permanent link to this record | |||||
| Author | Diego Cheda; Daniel Ponsa; Antonio Lopez | ||||
| Title | Pedestrian Candidates Generation using Monocular Cues | Type | Conference Article | ||
| Year | 2012 | Publication | IEEE Intelligent Vehicles Symposium | Abbreviated Journal | |
| Volume | Issue | Pages | 7-12 | ||
| Keywords | pedestrian detection | ||||
| Abstract | Common techniques for pedestrian candidates generation (e.g., sliding window approaches) are based on an exhaustive search over the image. This implies that the number of windows produced is huge, which translates into a significant time consumption in the classification stage. In this paper, we propose a method that significantly reduces the number of windows to be considered by a classifier. Our method is a monocular one that exploits geometric and depth information available on single images. Both representations of the world are fused together to generate pedestrian candidates based on an underlying model which is focused only on objects standing vertically on the ground plane and having certain height, according with their depths on the scene. We evaluate our algorithm on a challenging dataset and demonstrate its application for pedestrian detection, where a considerable reduction in the number of candidate windows is reached. | ||||
| Address | |||||
| Corporate Author | Thesis | ||||
| Publisher | IEEE Xplore | Place of Publication |
Editor | ||
| Language | Summary Language | Original Title | |||
| Series Editor | Series Title | Abbreviated Series Title | |||
| Series Volume | Series Issue | Edition | |||
| ISSN | 1931-0587 | ISBN | 978-1-4673-2119-8 | Medium | |
| Area | Expedition | Conference | IV | ||
| Notes | ADAS | Approved | no | ||
| Call Number | Admin @ si @ CPL2012c; ADAS @ adas @ cpl2012d | Serial | 2013 | ||
| Permanent link to this record | |||||