Multi-scale object candidates for generic object tracking in street scenes
By Aljosa Osep, Alexander Hermans, Francis Engelmann, Dirk Klostermann, Markus Mathias, Bastian Leibe. 2016
44 citations recorded by the source corpus.
Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Vittorio Ferrari
- Alexander Hermans
- Markus Mathias
- Bastian Leibe
- Jasper R. R. Uijlings
- Holger Caesar
- Luc Van Gool
- Roberto Cipolla
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Xiaogang Wang
- Andrew Zisserman
- Bernt Schiele
- Jitendra Malik
- Yi Yang
- Dacheng Tao
- Ming-Hsuan Yang
Citations in the corpus
- COCO-Stuff: Thing and Stuff Classes in Context (2016, methodology): “f people is sometimes considered a crowd). Several works have shown that different techniques are Dataset Images Classes Stuff classes Thing classes Year MSRC 21 [45] 591 21 6 15 2006 KITTI semantics [40] 203 14 9 4 2016 CamVid [11] 700 32 13 15 2008 Cityscapes [16] 25,000 30 13 14 2016 SIFT Flow [3”
- Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes (2016, methodology): “In automotive scenarios it is used in various ways, e.g . as a pre-processing step to discard image regions that are unlikely to contain objects of interest [42, 15], to improve object detection [4, 23, 24, 58], or in combination with 3D scene geometry [32, 17, 35].”