Weakly Supervised Training of Monocular 3D Object Detectors Using Wide Baseline Multi-view Traffic Camera Data

TL;DR

This paper introduces a weakly supervised method for training monocular 3D vehicle detectors using multi-view traffic camera data, leveraging existing datasets and geometric consistency to improve pose prediction accuracy.

Contribution

The paper proposes a novel weakly supervised fine-tuning approach for monocular 3D vehicle detection using multi-view data and geometric loss, addressing limitations of existing datasets.

Findings

Achieves competitive 7DoF vehicle pose prediction accuracy.

Leverages large autonomous vehicle datasets for pre-training.

Introduces a multi-view reprojection loss for geometric consistency.

Abstract

Accurate 7DoF prediction of vehicles at an intersection is an important task for assessing potential conflicts between road users. In principle, this could be achieved by a single camera system that is capable of detecting the pose of each vehicle but this would require a large, accurately labelled dataset from which to train the detector. Although large vehicle pose datasets exist (ostensibly developed for autonomous vehicles), we find training on these datasets inadequate. These datasets contain images from a ground level viewpoint, whereas an ideal view for intersection observation would be elevated higher above the road surface. We develop an alternative approach using a weakly supervised method of fine tuning 3D object detectors for traffic observation cameras; showing in the process that large existing autonomous vehicle datasets can be leveraged for pre-training. To fine-tune the monocular 3D object detector, our method utilises multiple 2D detections from overlapping, wide-baseline views and a loss that encodes the subjacent geometric consistency. Our method achieves vehicle 7DoF pose prediction accuracy on our dataset comparable to the top performing monocular 3D object detectors on autonomous vehicle datasets. We present our training methodology, multi-view reprojection loss, and dataset.