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Old Age
End-to-end Learning of Multi-sensor 3D Tracking by Detection
June 29, 2018 Β· Declared Dead Β· π IEEE International Conference on Robotics and Automation
"No code URL or promise found in abstract"
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Authors
Davi Frossard, Raquel Urtasun
arXiv ID
1806.11534
Category
cs.CV: Computer Vision
Cross-listed
cs.LG,
cs.RO
Citations
128
Venue
IEEE International Conference on Robotics and Automation
Last Checked
3 months ago
Abstract
In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.
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