End-To-End Learning of Multi-Sensor 3D Tracking by Detection

Davi Frossard1, Raquel Urtasun2

  • 1Uber Advanced Technologies Group, University of Toronto
  • 2University of Toronto

Details

10:30 - 13:00 | Tue 22 May | podL | [email protected]

Session: Visual Tracking 1

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.