Name
Abstract | ||
|---|---|---|
High performance vehicle detection and pose esti- mation in RGB images is essential for driver assistance systems as well as for autonomous vehicles. Classical 2D box-based detection schemes allow roughly estimating the position of other vehicles, but not their orientation relative to the ego-vehicle. Recent approaches use 3D models to derive the pose of other vehicles from single monocular images but do not reach real- time performance. In this paper we present an approach that achieves competitive performance on the challenging KITTI Object Detection and orientation Estimation benchmark while being the fastest approach with over 40 FPS. The key is a novel representation named MergeBox whose parameters can be estimated extremely efficiently. We extend SSD-a current fast state-of-the-art 2D box object detector- with this representation to our MB-Net. In contrast to all other current state-of-the-art methods we do not require explicit information on the object orientation for training our model. This reduces label costs significantly, a further advantage for practical applications that require labeling of databases that are much bigger than those used for research. |
Year | Venue | Field |
|---|---|---|
2018 | Intelligent Vehicles Symposium | Computer vision,Object detection,Computer science,Advanced driver assistance systems,Vehicle detection,Lidar,Solid modeling,RGB color model,Artificial intelligence,Monocular,Detector |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nils Gahlert | 1 | 0 | 0.34 |
| Marina Mayer | 2 | 0 | 0.34 |
| Lukas Schneider | 3 | 26 | 3.22 |
| Uwe Franke | 4 | 1702 | 115.13 |
| Joachim Denzler | 5 | 985 | 103.50 |