Name
Title | ||
|---|---|---|
A Stixel Approach For Enhancing Semantic Image Segmentation Using Prior Map Information |
Abstract | ||
|---|---|---|
A key problem for autonomous car navigation is the understanding, at an object level, of the current driving situation. Addressing this issue requires the extraction of meaningful information from on-board stereo imagery by classifying the fundamental elements of urban scenes into semantic categories that can more easily be interpreted and be reflected upon (streets, buildings, pedestrians, vehicles, signs, etc.). A probabilistic method is proposed to fuse a coarse prior 3D map data with stereo imagery classification. A novel fusion architecture based on the Stixel framework is presented for combining semantic pixel-wise segmentation from a convolutional neural network (CNN) with depth information obtained from stereo imagery while integrating coarse prior depth and label information. The proposed approach was tested on a manually labeled data set in urban environments. The results show that the classification accuracy of the fundamental elements composing the urban scene was significantly enhanced by this method compared to what is obtained from the semantic pixel-wise segmentation of a CNN alone. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/ICARCV.2018.8581150 | 2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) |
Field | DocType | ISSN |
Architecture,Pattern recognition,Convolutional neural network,Computer science,Segmentation,Probabilistic method,Control engineering,Semantic image segmentation,Artificial intelligence,Labeled data,Fuse (electrical) | Conference | 2474-2953 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sylvain Jonchery | 1 | 0 | 0.34 |
| Guillaume Bresson | 2 | 20 | 5.83 |
| Bruno Vallet | 3 | 24 | 1.68 |
| Rafal Zbikowski | 4 | 0 | 0.34 |