Exploiting Motion Prior for Accurate Pose Estimation of Dashboard Cameras

TL;DR

This paper introduces a novel pose estimation method for dashcam images that leverages inherent camera motion priors, significantly improving accuracy and robustness despite low image quality and dynamic scenes.

Contribution

The study proposes a new pose regression module that learns and exploits camera motion priors, enhancing pose estimation accuracy for challenging dashcam footage.

Findings

22% improvement over baseline in pose estimation accuracy

19% more images successfully estimated in SfM

Reduced reprojection error in pose estimates

Abstract

Dashboard cameras (dashcams) record millions of driving videos daily, offering a valuable potential data source for various applications, including driving map production and updates. A necessary step for utilizing these dashcam data involves the estimation of camera poses. However, the low-quality images captured by dashcams, characterized by motion blurs and dynamic objects, pose challenges for existing image-matching methods in accurately estimating camera poses. In this study, we propose a precise pose estimation method for dashcam images, leveraging the inherent camera motion prior. Typically, image sequences captured by dash cameras exhibit pronounced motion prior, such as forward movement or lateral turns, which serve as essential cues for correspondence estimation. Building upon this observation, we devise a pose regression module aimed at learning camera motion prior, subsequently integrating these prior into both correspondences and pose estimation processes. The experiment shows that, in real dashcams dataset, our method is 22% better than the baseline for pose estimation in AUC5\textdegree, and it can estimate poses for 19% more images with less reprojection error in Structure from Motion (SfM).