Method Details
Details for method 'Instance-level Segmentation of Vehicles by Deep Contours'
Method overview
| name | Instance-level Segmentation of Vehicles by Deep Contours |
| challenge | instance-level semantic labeling |
| details | Our method uses the fully convolutional network (FCN) for semantic labeling and for estimating the boundary of each vehicle. Even though a contour is in general a one pixel wide structure which cannot be directly learned by a CNN, our network addresses this by providing areas around the contours. Based on these areas, we separate the individual vehicle instances. |
| publication | Instance-level Segmentation of Vehicles by Deep Contours Jan van den Brand, Matthias Ochs and Rudolf Mester Asian Conference on Computer Vision - Workshop on Computer Vision Technologies for Smart Vehicle |
| project page / code | |
| used Cityscapes data | fine annotations |
| used external data | ImageNet |
| runtime | 0.2 s GTX 1070 |
| subsampling | 2 |
| submission date | August, 2016 |
| previous submissions |
Average results
| Metric | Value |
|---|---|
| AP | 2.26995 |
| AP50% | 3.6534 |
| AP100m | 3.87795 |
| AP50m | 4.87125 |
Class results
| Class | AP | AP50% | AP100m | AP50m |
|---|---|---|---|---|
| person | 0 | 0 | 0 | 0 |
| rider | 0 | 0 | 0 | 0 |
| car | 18.1596 | 29.2272 | 31.0236 | 38.97 |
| truck | 0 | 0 | 0 | 0 |
| bus | 0 | 0 | 0 | 0 |
| train | 0 | 0 | 0 | 0 |
| motorcycle | 0 | 0 | 0 | 0 |
| bicycle | 0 | 0 | 0 | 0 |