Typography-Based Monocular Distance Estimation for Advanced Driver-Assistance Systems
- URL: http://arxiv.org/abs/2607.00319v1
- Date: Wed, 01 Jul 2026 01:35:07 GMT
- Title: Typography-Based Monocular Distance Estimation for Advanced Driver-Assistance Systems
- Abstract summary: Estimating the distance to a leading vehicle is a basic input to forward collision warning, adaptive cruise control, and automated emergency braking.<n>This paper asks whether a single ordinary camera can recover the same distance by using a target that is standardized in size and present on every road vehicle: the rear license plate.<n>The proposed method detects the plate, measures the height of its printed characters, identifies the issuing state to select the correct physical character height, and recovers distance from the camera projection.
- Score: 4.601043621601829
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Estimating the distance to a leading vehicle is a basic input to forward collision warning, adaptive cruise control, and automated emergency braking. Production systems obtain this distance from radar, laser scanners, or stereo camera pairs, which add cost, power draw, and packaging constraints. This paper asks whether a single ordinary camera can recover the same distance by using a target that is standardized in size and present on every road vehicle: the rear license plate. U.S. plates share a fixed outer size and a character height that is set by regulation and varies only narrowly between states, so the height of a plate character in the image is a direct measure of distance once the camera geometry is known. The proposed method (Typography-Based Monocular Distance Estimation) detects the plate, measures the height of its printed characters, identifies the issuing state to select the correct physical character height, and recovers distance from the camera projection. Three measurements taken from the same plate: the character height, the stroke width, and the character spacing. Together with the spacing of the two mounting holes and a single-image depth network, are combined so that a weak or corrupted measurement is given less weight automatically. The distance, its rate of change, and a time-to-collision estimate are smoothed across frames and used to raise a warning with the timing used by U.S. collision-warning regulations. The same plate that anchors the scale also identifies the vehicle, so the method returns a distance, a bearing, and an identity from one passive sensor. It reads scale from a printed standard instead of from time of flight or parallax, making it a cheap, low-maintenance complement to those sensors in a fault-tolerant perception stack, achieving the cost-effective distance estimation with error less than 0.13 m.
Related papers
- OmniPoint: Universal Monocular Metric Pointcloud from Any Camera [79.24417597712802]
We present OmniPoint, a unified framework designed to generalize metric reconstruction across diverse imaging sensors.<n>To overcome projection rigidity, OmniPoint abandons conventional planar depth regression.<n>It instead adopts a decoupled ray and distance representation alongside a decoupled training objective.
arXiv Detail & Related papers (2026-09-08T19:45:58Z) - A Height-Constrained 2-Point Minimal Solver for Pose Estimation from Active LED Markers with Event Cameras [51.7262911415172]
We develop an event-based active marker system to collect real-world data with ground truth from a motion capture system.<n>Experiments on both synthetic and real data demonstrate improved accuracy over the state-of-the-art P2P solver.
arXiv Detail & Related papers (2026-08-10T12:19:24Z) - Knowledge-Driven Dimension Estimation from a Single Image -3D Asset Generation Technology for Digital Twin Construction [0.0]
This paper proposes a method for estimating the scale of an object by decomposing it into multiple structural elements.<n>It generates a 3D asset of the object by reconstructing the estimated components.<n>This method makes it possible to place 3D assets with a scale approximating the real environment within a digital twin space.
arXiv Detail & Related papers (2026-06-29T20:35:54Z) - Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography [4.601043621601829]
Inter-vehicle distance estimation is a cornerstone of Advanced Driver Assistance Systems (ADAS) and autonomous driving.<n>Monocular camera-based estimation offers a low-cost alternative but suffers from fundamental scale ambiguity.<n>Recent deep learning methods for monocular depth achieve impressive results yet require expensive supervised training, suffer from domain shift, and produce predictions that are difficult to certify for safety-critical deployment.<n>This paper presents a framework that exploits the standardized typography of United States license plates as passive fiducial markers for metric ranging.
arXiv Detail & Related papers (2026-04-14T03:27:44Z) - Typography-Based Monocular Distance Estimation Framework for Vehicle Safety Systems [4.601043621601829]
This paper introduces a typography-based monocular distance estimation framework.<n>It exploits the standardized typography of license plates as passive fiducial markers for metric distance estimation.<n>A comprehensive comparison with a plate-width based method shows that character-based ranging reduces the standard deviation of estimates by 35%.
arXiv Detail & Related papers (2026-03-24T04:18:19Z) - Energy-Based Models for Cross-Modal Localization using Convolutional
Transformers [52.27061799824835]
We present a novel framework for localizing a ground vehicle mounted with a range sensor against satellite imagery in the absence of GPS.
We propose a method using convolutional transformers that performs accurate metric-level localization in a cross-modal manner.
We train our model end-to-end and demonstrate our approach achieving higher accuracy than the state-of-the-art on KITTI, Pandaset, and a custom dataset.
arXiv Detail & Related papers (2023-06-06T21:27:08Z) - Multi-Modal 3D Object Detection by Box Matching [109.43430123791684]
We propose a novel Fusion network by Box Matching (FBMNet) for multi-modal 3D detection.
With the learned assignments between 3D and 2D object proposals, the fusion for detection can be effectively performed by combing their ROI features.
arXiv Detail & Related papers (2023-05-12T18:08:51Z) - LET-3D-AP: Longitudinal Error Tolerant 3D Average Precision for Camera-Only 3D Detection [26.278496981844317]
We propose variants of the 3D AP metric to be more permissive with respect to depth estimation errors.
Specifically, our novel longitudinal error tolerant metrics, LET-3D-AP and LET-3D-APL, allow longitudinal localization errors up to a given tolerance.
We find that state-of-the-art camera-based detectors can outperform popular LiDAR-based detectors with our new metrics past 10% depth error tolerance.
arXiv Detail & Related papers (2022-06-15T17:57:41Z) - Single View Physical Distance Estimation using Human Pose [18.9877515094788]
We propose a fully automated system that simultaneously estimates the camera intrinsics, the ground plane, and physical distances between people from a single RGB image or video.
The proposed approach enables existing camera systems to measure physical distances without needing a dedicated calibration process or range sensors.
We contribute to the publicly available MEVA dataset with additional distance annotations, resulting in MEVADA -- the first evaluation benchmark in the world for the pose-based auto-calibration and distance estimation problem.
arXiv Detail & Related papers (2021-06-18T19:50:40Z) - Geometry-based Distance Decomposition for Monocular 3D Object Detection [48.63934632884799]
We propose a novel geometry-based distance decomposition to recover the distance by its factors.
The decomposition factors the distance of objects into the most representative and stable variables.
Our method directly predicts 3D bounding boxes from RGB images with a compact architecture.
arXiv Detail & Related papers (2021-04-08T13:57:30Z) - Accurate Alignment Inspection System for Low-resolution Automotive and
Mobility LiDAR [125.41260574344933]
An accurate inspection system is proposed for estimating a LiDAR alignment error after sensor attachment on a mobility system such as a vehicle or robot.
The proposed method uses only a single target board at the fixed position to estimate the three orientations (roll, tilt, and yaw) and the horizontal position of the LiDAR attachment with sub-degree and millimeter level accuracy.
arXiv Detail & Related papers (2020-08-24T17:47:59Z) - LIBRE: The Multiple 3D LiDAR Dataset [54.25307983677663]
We present LIBRE: LiDAR Benchmarking and Reference, a first-of-its-kind dataset featuring 10 different LiDAR sensors.
LIBRE will contribute to the research community to provide a means for a fair comparison of currently available LiDARs.
It will also facilitate the improvement of existing self-driving vehicles and robotics-related software.
arXiv Detail & Related papers (2020-03-13T06:17:39Z) - 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.