Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation

• URL: http://arxiv.org/abs/2609.05523v1
• Date: Tue, 01 Sep 2026 09:21:14 GMT
• Title: Infrastructure-based Monocular 3D Vehicle Localization Framework with Experimental Validation
• Abstract summary: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame.<n>We develop a data-collection and label-generation pipeline based on synchronized video from a roadside camera and an unmanned aerial vehicle.<n>Results show that the proposed method can recover vehicle trajectories and orientations from monocular roadside imagery without a separate geometric post-processing stage.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This paper presents a one-stage learning framework that maps monocular roadside-camera images directly to vehicle states in a ground-fixed coordinate frame. Unlike conventional approaches that first detect vehicles in the image plane and subsequently apply geometric post-processing, the proposed method leverages features from a pretrained object detector to jointly estimate each vehicle's ground-plane position, dimensions, and yaw angle. The framework therefore uses visual features not only for vehicle detection but also for direct spatial and orientation estimation. To support model training and evaluation, we develop a data-collection and label-generation pipeline based on synchronized video from a roadside camera and an unmanned aerial vehicle (UAV). Acting as a temporary top-view sensing platform, the UAV provides vehicle trajectories, dimensions, and orientations, which are transformed into the ground-fixed coordinate frame and temporally aligned with the roadside-camera images to generate ground-truth labels. The framework is evaluated using data collected during multiple experiments at the Mcity Test Facility. Results show that the proposed method can recover vehicle trajectories and orientations from monocular roadside imagery without a separate geometric post-processing stage, demonstrating its potential as a scalable approach to infrastructure-based perception at urban intersections.

Related papers

• Mobile Traffic Camera Calibration from Road Geometry for UAV-Based Traffic Surveillance [0.1019561860229868]
  Raw UAV video is difficult to use for traffic analytics because vehicle motion is observed in perspective image coordinates.<n>This paper presents a lightweight pipeline for converting monocular UAV traffic video into a local metric bird's-eye-view representation.
  arXiv  Detail & Related papers  (2026-05-12T10:14:43Z)
• GHOST: Ground-projected Hypotheses from Observed Structure-from-Motion Trajectories [1.9318925338937918]
  We present a scalable self-supervised approach for segmenting vehicle trajectories from monocular images for autonomous driving.<n>We treat recorded ego-vehicle motion as implicit supervision and recover camera trajectories via monocular structure-from-motion.<n>Our results indicate that large-scale ego-motion distillation yields structured and generalizable path proposals.
  arXiv  Detail & Related papers  (2026-03-21T00:47:03Z)
• Boosting 3-DoF Ground-to-Satellite Camera Localization Accuracy via Geometry-Guided Cross-View Transformer [66.82008165644892]
  We propose a method to increase the accuracy of a ground camera's location and orientation by estimating the relative rotation and translation between the ground-level image and its matched/retrieved satellite image. Experimental results demonstrate that our method significantly outperforms the state-of-the-art.
  arXiv  Detail & Related papers  (2023-07-16T11:52:27Z)
• Street-View Image Generation from a Bird's-Eye View Layout [95.36869800896335]
  Bird's-Eye View (BEV) Perception has received increasing attention in recent years. Data-driven simulation for autonomous driving has been a focal point of recent research. We propose BEVGen, a conditional generative model that synthesizes realistic and spatially consistent surrounding images.
  arXiv  Detail & Related papers  (2023-01-11T18:39:34Z)
• Uncertainty-aware Vision-based Metric Cross-view Geolocalization [25.87104194833264]
  We present an end-to-end differentiable model that uses the ground and aerial images to predict a probability distribution over possible vehicle poses. We improve the previous state-of-the-art by a large margin even without ground or aerial data from the test region.
  arXiv  Detail & Related papers  (2022-11-22T10:23:20Z)
• Monocular BEV Perception of Road Scenes via Front-to-Top View Projection [57.19891435386843]
  We present a novel framework that reconstructs a local map formed by road layout and vehicle occupancy in the bird's-eye view. Our model runs at 25 FPS on a single GPU, which is efficient and applicable for real-time panorama HD map reconstruction.
  arXiv  Detail & Related papers  (2022-11-15T13:52:41Z)
• Satellite Image Based Cross-view Localization for Autonomous Vehicle [59.72040418584396]
  This paper shows that by using an off-the-shelf high-definition satellite image as a ready-to-use map, we are able to achieve cross-view vehicle localization up to a satisfactory accuracy. Our method is validated on KITTI and Ford Multi-AV Seasonal datasets as ground view and Google Maps as the satellite view.
  arXiv  Detail & Related papers  (2022-07-27T13:16:39Z)
• Monocular 3D Vehicle Detection Using Uncalibrated Traffic Cameras through Homography [12.062095895630563]
  This paper proposes a method to extract the position and pose of vehicles in the 3D world from a single traffic camera. We observe that the homography between the road plane and the image plane is essential to 3D vehicle detection. We propose a new regression target called textittailedr-box and a textitdual-view network architecture which boosts the detection accuracy on warped BEV images.
  arXiv  Detail & Related papers  (2021-03-29T02:57:37Z)
• Self-Supervised Steering Angle Prediction for Vehicle Control Using Visual Odometry [55.11913183006984]
  We show how a model can be trained to control a vehicle's trajectory using camera poses estimated through visual odometry methods. We propose a scalable framework that leverages trajectory information from several different runs using a camera setup placed at the front of a car.
  arXiv  Detail & Related papers  (2021-03-20T16:29:01Z)
• Road Curb Detection and Localization with Monocular Forward-view Vehicle Camera [74.45649274085447]
  We propose a robust method for estimating road curb 3D parameters using a calibrated monocular camera equipped with a fisheye lens. Our approach is able to estimate the vehicle to curb distance in real time with mean accuracy of more than 90%.
  arXiv  Detail & Related papers  (2020-02-28T00:24:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.