End-to-end Learning of Multi-sensor 3D Tracking by Detection

Davi Frossard; Raquel Urtasun

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End-to-end Learning of Multi-sensor 3D Tracking by Detection

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arxiv 1806.11534 v1 pith:ZWNZRMVI submitted 2018-06-29 cs.CV cs.LGcs.RO

End-to-end Learning of Multi-sensor 3D Tracking by Detection

Davi Frossard , Raquel Urtasun This is my paper

classification cs.CV cs.LGcs.RO

keywords detectionend-to-endtrackingverywellaccurateapproachcameras

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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.

End-to-end Learning of Multi-sensor 3D Tracking by Detection

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