Joint Monocular 3D Vehicle Detection and Tracking

11/26/2018 ∙ by Hou-Ning Hu, et al. ∙ 0

3D vehicle detection and tracking from a monocular camera requires detecting and associating vehicles, and estimating their locations and extents together. It is challenging because vehicles are in constant motion and it is practically impossible to recover the 3D positions from a single image. In this paper, we propose a novel framework that jointly detects and tracks 3D vehicle bounding boxes. Our approach leverages 3D pose estimation to learn 2D patch association overtime and uses temporal information from tracking to obtain stable 3D estimation. Our method also leverages 3D box depth ordering and motion to link together the tracks of occluded objects. We train our system on realistic 3D virtual environments, collecting a new diverse, large-scale and densely annotated dataset with accurate 3D trajectory annotations. Our experiments demonstrate that our method benefits from inferring 3D for both data association and tracking robustness, leveraging our dynamic 3D tracking dataset.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 1

page 3

page 5

page 8

page 11

page 13

page 14

This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.