Name
Abstract | ||
|---|---|---|
An "elephant in the room" for most current object detection and localization methods is the lack of explicit modelling of partial visibility due to occlusion by other objects or truncation by the image boundary. Based on a sliding window approach, we propose a detection method which explicitly models partial visibility by treating it as a latent variable. A novel non-maximum suppression scheme is proposed which takes into account the inferred partial visibility of objects while providing a globally optimal solution. The method gives more detailed scene interpretations than conventional detectors in that we are able to identify the visible parts of an object. We report improved average precision on the PASCAL VOC 2010 dataset compared to a baseline detector. |
Year | Venue | Field |
|---|---|---|
2013 | CoRR | Object detection,Truncation,Computer vision,Visibility,Sliding window protocol,Pattern recognition,Computer science,Latent variable,Artificial intelligence,Detector |
DocType | Volume | Citations |
Journal | abs/1311.6758 | 0 |
PageRank | References | Authors |
0.34 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Patrick Ott | 1 | 50 | 3.62 |
| Mark Everingham | 2 | 5232 | 282.93 |
| Jiri Matas | 3 | 335 | 35.85 |