Method Details
Details for method 'Foveal Vision for Instance Segmentation of Road Images'
Method overview
| name | Foveal Vision for Instance Segmentation of Road Images |
| challenge | instance-level semantic labeling |
| details | Directly based on 'Pixel-level Encoding for Instance Segmentation'. Adds an improved angular distance measure and a foveal concept to better address small objects at the vanishing point of the road. |
| publication | Foveal Vision for Instance Segmentation of Road Images Benedikt Ortelt, Christian Herrmann, Dieter Willersinn, Jürgen Beyerer VISAPP 2018 |
| project page / code | |
| used Cityscapes data | fine annotations, stereo |
| used external data | ImageNet |
| runtime | n/a |
| subsampling | no |
| submission date | September, 2017 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 12.5006 |
| AP50% | 25.2121 |
| AP100m | 20.361 |
| AP50m | 22.1157 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 13.3986 | 31.4868 | 24.512 | 24.72 |
| rider | 11.4223 | 29.6768 | 19.6153 | 20.1893 |
| car | 24.4889 | 39.9936 | 39.2834 | 42.4629 |
| truck | 9.35526 | 15.9759 | 14.5412 | 17.151 |
| bus | 14.497 | 23.7958 | 24.1934 | 27.6372 |
| train | 12.1842 | 21.6991 | 18.4973 | 21.8025 |
| motorcycle | 7.97874 | 19.2051 | 11.0959 | 11.6755 |
| bicycle | 6.67976 | 19.8637 | 11.1491 | 11.2875 |