Monocular 3D Detection and reID-enhanced Tracking of Multiple Traffic Participants

Alexander Sing, Csaba Beleznai, Kai Göbel

2022

Abstract

Autonomous driving is becoming a major scientific challenge and applied domain of significant impact, also triggering a demand for the enhanced safety of vulnerable road users, such as cyclists and pedestrians. The recent developments in Deep Learning have demonstrated that monocular 3D pose estimation is a potential detection modality in safety related task domains such as perception for autonomous driving and automated traffic monitoring. Deep Learning offers enhanced ways to represent targets in terms of their location, shape, appearance and motion. Learning can capture the significant variations seen in the training data while retaining class- or target-specific cues. Learning even allows for discovering specific correlations within an image of a 3D scene, as a perspective image contains many hints about an object’s 3D location, orientation, size and identity. In this paper we propose an attention-based representational enhancement to enhance the spatial accuracy of 3d pose and the temporal stability of multi-target tracking. The presented methodology is evaluated on the KITTI multi-target tracking benchmark. It demonstrates competitive results against other recent techniques, and when compared to a baseline relying solely on a Kalman-Filter-based kinematic association step.

Paper Citation

in Harvard Style

Sing A., Beleznai C. and Göbel K. (2022). Monocular 3D Detection and reID-enhanced Tracking of Multiple Traffic Participants. In Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS; ISBN 978-989-758-611-8, SciTePress, pages 426-434. DOI: 10.5220/0011527300003332

in Bibtex Style

@conference{robovis22,
author={Alexander Sing and Csaba Beleznai and Kai Göbel},
title={Monocular 3D Detection and reID-enhanced Tracking of Multiple Traffic Participants},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS},
year={2022},
pages={426-434},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011527300003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence - Volume 1: ROBOVIS
TI - Monocular 3D Detection and reID-enhanced Tracking of Multiple Traffic Participants
SN - 978-989-758-611-8
AU - Sing A.
AU - Beleznai C.
AU - Göbel K.
PY - 2022
SP - 426
EP - 434
DO - 10.5220/0011527300003332
PB - SciTePress