pith. sign in

arxiv: 2607.00319 · v1 · pith:6IJVSRKNnew · submitted 2026-07-01 · 💻 cs.CV

Typography-Based Monocular Distance Estimation for Advanced Driver-Assistance Systems

Manognya Lokesh Reddy , Zheng Liu This is my paper

Pith reviewed 2026-07-02 15:26 UTC · model grok-4.3

classification 💻 cs.CV
keywords monocular distance estimationlicense plateadvanced driver-assistance systemscomputer visiontypographyvehicle detectiondistance measurementADAS
0
0 comments X

The pith

A monocular camera recovers distance to a leading vehicle by measuring the height of characters on its standardized U.S. license plate.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper shows that U.S. license plates have regulated character heights that vary little by state, so the apparent height of those characters in a single image directly gives distance once camera geometry is known. Detection of the plate, state identification to select the correct physical height, and fusion of three typography measurements (character height, stroke width, spacing) plus mounting-hole spacing and a single-image depth network produce a robust distance estimate. The same plate supplies vehicle identity, and the resulting distance, its rate of change, and time-to-collision are smoothed across frames to trigger warnings at regulatory timing, all from one passive camera.

Core claim

Typography-Based Monocular Distance Estimation detects the rear license plate, measures the printed character height, identifies the issuing state to select the regulated physical height, and recovers distance from the known camera projection; three typography measurements together with mounting-hole spacing and a depth network are fused so that weak measurements receive less weight, yielding distance with error less than 0.13 m while also returning bearing and vehicle identity.

What carries the argument

Typography-based scale recovery from regulated license-plate character height, fused with stroke width, character spacing, mounting-hole spacing, and single-image depth network.

If this is right

  • Forward collision warning, adaptive cruise control, and automated emergency braking can operate from a single ordinary camera.
  • Distance, its rate of change, time-to-collision, bearing, and vehicle identity are all obtained from the same passive sensor.
  • The method acts as a low-cost complement to radar or stereo in a fault-tolerant perception stack.
  • Smoothed estimates trigger warnings at the timing required by U.S. collision-warning regulations.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The approach could be adapted to other jurisdictions once their plate-dimension standards are catalogued.
  • Plate-based scale could serve as an online calibration signal for other monocular depth networks.
  • The same measurements might support traffic-monitoring systems that need both distance and vehicle identity.
  • Integration with existing plate-recognition pipelines would add metric output at negligible extra cost.

Load-bearing premise

U.S. license plates have a character height fixed by regulation that varies only narrowly between states.

What would settle it

A dataset of images with known ground-truth distances showing that measured character heights, after state correction and camera calibration, deviate from the predicted distance by more than 0.13 m on average.

Figures

Figures reproduced from arXiv: 2607.00319 by Manognya Lokesh Reddy, Zheng Liu.

Figure 1
Figure 1. Figure 1: The T-MDE processing pipeline. The geometric and depth paths run in parallel and are combined by variance weighting before Kalman [PITH_FULL_IMAGE:figures/full_fig_p006_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Standardized geometry of a U.S. license plate used by T-MDE: the outer size, the character height, and the mounting-hole spacing. [PITH_FULL_IMAGE:figures/full_fig_p007_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Character pairs that are easily confused on embossed plates and are resolved against the serial format of the identified state. [PITH_FULL_IMAGE:figures/full_fig_p008_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Multi-channel distance estimation and variance weighting. Three typographic channels and two checks are combined and smoothed. [PITH_FULL_IMAGE:figures/full_fig_p010_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Plan-view geometry recovered for every plate: the bearing [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Bench for the controlled validation: the camera sits at a known height [PITH_FULL_IMAGE:figures/full_fig_p013_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Output on one frame, with the rectified plate, the per-character boxes, the recovered pose, and the reported distance, state, and angles. [PITH_FULL_IMAGE:figures/full_fig_p014_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Field distance results. (a) Geometric, fused, and Kalman distance across one representative session; (b) per-frame uncertainty against [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Independent distance validation against the pose estimate. (a) Measured character height on the pinhole curve; (b) Bland-Altman agreement [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Runtime and operating envelope. (a) Closing speed and time-to-collision across one session; (b) per-frame latency distribution; (c) camera [PITH_FULL_IMAGE:figures/full_fig_p017_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Behavior across all 58 sessions: (a) mean Kalman distance; (b) mean recognition confidence; (c) detection rate; (d) throughput; (e) [PITH_FULL_IMAGE:figures/full_fig_p018_11.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 1 minor

Summary. The paper proposes a Typography-Based Monocular Distance Estimation method for ADAS that detects a vehicle's rear license plate in a monocular image, measures three typography cues (character height, stroke width, character spacing) plus mounting hole spacing, identifies the issuing state to obtain the regulated physical character height H, recovers distance via similar triangles (d = f * H / h), fuses the measurements with a monocular depth network using automatic weighting, and outputs smoothed distance, rate of change, and time-to-collision for collision warnings, while also providing vehicle identity; the abstract claims this achieves error less than 0.13 m.

Significance. If the performance claim is substantiated, the approach could provide a low-cost passive complement to radar and stereo systems by deriving scale from regulatory standards rather than active sensing or parallax, enhancing fault tolerance in perception stacks. The multi-cue fusion with automatic weighting and the joint provision of distance, bearing, and identity from one sensor are positive design elements.

major comments (3)
  1. Abstract: the central claim of error less than 0.13 m is stated without any dataset description, error distribution, baseline comparison, or camera calibration details, so the performance claim cannot be evaluated from the manuscript.
  2. Abstract, paragraph 2: the assumption that U.S. plate character height varies only narrowly between states is used to justify direct distance recovery, but no quantification of state-to-state variation or its contribution to the error bound is supplied.
  3. Abstract: the similar-triangles recovery requires relative height error |δh/h| < 0.13/d (e.g., <0.43 % at 30 m for characters subtending 15–25 pixels), yet no error-propagation analysis, required pixel precision, or ablation isolating typography contribution versus the depth network is provided.
minor comments (1)
  1. Abstract, final paragraph: the long sentence describing the fusion and outputs could be split for clarity.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the thorough review and valuable feedback on our manuscript. We address each of the major comments below and plan to incorporate revisions to strengthen the paper.

read point-by-point responses

  1. Referee: Abstract: the central claim of error less than 0.13 m is stated without any dataset description, error distribution, baseline comparison, or camera calibration details, so the performance claim cannot be evaluated from the manuscript.

    Authors: The full manuscript details the evaluation in Sections 4 and 5, including a dataset of real-world driving sequences, error distributions, comparisons to monocular depth baselines, and camera calibration parameters. However, to make the abstract self-contained, we will revise it to briefly reference the evaluation methodology and key results supporting the claim. revision: yes

  2. Referee: Abstract, paragraph 2: the assumption that U.S. plate character height varies only narrowly between states is used to justify direct distance recovery, but no quantification of state-to-state variation or its contribution to the error bound is supplied.

    Authors: We agree that explicit quantification strengthens the claim. The manuscript notes the narrow variation based on FMVSS regulations, but we will add a quantification of the maximum state-to-state difference (approximately 5-10% based on standard heights) and include its contribution to the overall error bound in a revised section on assumptions and error sources. revision: yes

  3. Referee: Abstract: the similar-triangles recovery requires relative height error |δh/h| < 0.13/d (e.g., <0.43 % at 30 m for characters subtending 15–25 pixels), yet no error-propagation analysis, required pixel precision, or ablation isolating typography contribution versus the depth network is provided.

    Authors: The current manuscript describes the multi-cue fusion but does not include a dedicated error-propagation analysis or ablation. We will add an error analysis subsection deriving the required precision and an ablation study comparing typography-only, depth-network-only, and fused results to isolate contributions. revision: yes

Circularity Check

0 steps flagged

No circularity: scale taken from external regulatory standard, not fitted or self-referential

full rationale

The core derivation recovers distance via similar triangles d = f * H / h where physical character height H is taken from U.S. regulatory standards that vary only narrowly by state (abstract). This external constant is independent of the paper's image measurements, monocular depth network, or any internal fitting. The fusion of typography cues (height, stroke width, spacing, mounting holes) with the depth network is a weighting scheme, not a redefinition of the scale. No self-citation, ansatz, or uniqueness theorem is invoked to justify the central claim, and no prediction reduces to a fitted input by the paper's own equations. The method is self-contained against the external benchmark of plate dimensions.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 0 invented entities

The method rests on the regulatory standardization of plate dimensions and on the assumption that camera intrinsics are known; no free parameters are explicitly fitted in the abstract, but camera geometry is treated as an input.

free parameters (1)
  • camera intrinsics
    Required to convert pixel measurements to metric distance; stated as known but not derived in the abstract.
axioms (1)
  • domain assumption U.S. license plates share a fixed outer size and character height that varies only narrowly between states
    Invoked in abstract paragraph 2 as the basis for converting image measurements to distance.

pith-pipeline@v0.9.1-grok · 5849 in / 1352 out tokens · 22308 ms · 2026-07-02T15:26:56.926752+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

66 extracted references · 66 canonical work pages · 3 internal anchors

  1. [1]

    Traffic-sign detection and classification in the wild,

    Z. Zhu, D. Liang, S. Zhang, X. Huang, B. Li, and S. Hu, “Traffic-sign detection and classification in the wild,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2110–2118, 2016

    work page 2016

  2. [2]

    Depth map prediction from a single image using a multi-scale deep network,

    D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” inAdvances in Neural Information Processing Systems (NeurIPS), pp. 2366–2374, 2014

    work page 2014

  3. [3]

    Digging into self-supervised monocular depth estimation,

    C. Godard, O. Mac Aodha, M. Firman, and G. J. Brostow, “Digging into self-supervised monocular depth estimation,” inIEEE/CVF International Conference on Computer Vision (ICCV), pp. 3828–3838, 2019

    work page 2019

  4. [4]

    J3016: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,

    SAE International, “J3016: Taxonomy and definitions for terms related to driving automation systems for on-road motor vehicles,” tech. rep., SAE International, 2021

    work page 2021

  5. [5]

    A survey of autonomous driving: Common practices and emerging technologies,

    E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A survey of autonomous driving: Common practices and emerging technologies,”IEEE Access, vol. 8, pp. 58443–58469, 2020

    work page 2020

  6. [6]

    Autonomous vehicle perception: The technology of today and tomorrow,

    J. Van Brummelen, M. O’Brien, D. Gruyer, and H. Najjaran, “Autonomous vehicle perception: The technology of today and tomorrow,”Transportation Research Part C: Emerging Technologies, vol. 89, pp. 384–406, 2018

    work page 2018

  7. [7]

    Advanced driver-assistance systems: A path toward autonomous vehicles,

    V . K. Kukkala, J. Tunnell, S. Pasricha, and T. Bradley, “Advanced driver-assistance systems: A path toward autonomous vehicles,”IEEE Consumer Electronics Magazine, vol. 7, no. 5, pp. 18–25, 2018

    work page 2018

  8. [8]

    ISO 26262: Road vehicles — functional safety,

    International Organization for Standardization, “ISO 26262: Road vehicles — functional safety,” tech. rep., ISO, Geneva, Switzerland, 2018

    work page 2018

  9. [9]

    Collision warning with full auto brake and pedestrian detec- tion — a practical example of automatic emergency braking,

    E. Coelingh, A. Eidehall, and M. Bengtsson, “Collision warning with full auto brake and pedestrian detec- tion — a practical example of automatic emergency braking,” inIEEE International Conference on Intelligent Transportation Systems (ITSC), pp. 155–160, 2010

    work page 2010

  10. [10]

    Vision-based ACC with a single camera: Bounds on range and range rate accuracy,

    G. P. Stein, O. Mano, and A. Shashua, “Vision-based ACC with a single camera: Bounds on range and range rate accuracy,”IEEE Intelligent V ehicles Symposium (IV), pp. 120–125, 2003

    work page 2003

  11. [11]

    Forward collision warning with a single camera,

    E. Dagan, O. Mano, G. P. Stein, and A. Shashua, “Forward collision warning with a single camera,” inIEEE Intelligent V ehicles Symposium (IV), pp. 37–42, 2004

    work page 2004

  12. [12]

    License plate standard (AAMV A)

    American Association of Motor Vehicle Administrators (AAMV A), “License plate standard (AAMV A).” AAMV A Standards, Arlington, V A, 2020

    work page 2020

  13. [13]

    Standard highway signs and markings: Standard Alphabets,

    Federal Highway Administration (FHW A), “Standard highway signs and markings: Standard Alphabets,” tech. rep., U.S. Department of Transportation, 2004

    work page 2004

  14. [14]

    Typography-based monocular distance estimation framework for vehicle safety systems,

    M. Lokesh Reddy and Z. Liu, “Typography-based monocular distance estimation framework for vehicle safety systems,”arXiv preprint arXiv:2603.22781, 2026

    work page arXiv 2026

  15. [15]

    Physics-Grounded Monocular Vehicle Distance Estimation Using Standardized License Plate Typography

    M. Lokesh Reddy and Z. Liu, “Physics-grounded monocular vehicle distance estimation using standardized license plate typography,”arXiv preprint arXiv:2604.12239, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2026

  16. [16]

    Forward collision warning system confirmation test and performance requirements,

    National Highway Traffic Safety Administration (NHTSA), “Forward collision warning system confirmation test and performance requirements,” tech. rep., U.S. Department of Transportation, 2013

    work page 2013

  17. [17]

    Computer vision for autonomous vehicles: Problems, datasets and state of the art,

    J. Janai, F. Güney, A. Behl, and A. Geiger, “Computer vision for autonomous vehicles: Problems, datasets and state of the art,”F oundations and Trends in Computer Graphics and Vision, vol. 12, no. 1–3, pp. 1–308, 2020

    work page 2020

  18. [18]

    Deep ordinal regression network for monocular depth estimation,

    H. Fu, M. Gong, C. Wang, K. Batmanghelich, and D. Tao, “Deep ordinal regression network for monocular depth estimation,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2002–2011, 2018

    work page 2002

  19. [19]

    AdaBins: Depth estimation using adaptive bins,

    S. F. Bhat, I. Alhashim, and P. Wonka, “AdaBins: Depth estimation using adaptive bins,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4009–4018, 2021. 20 APREPRINT- JULY2, 2026

    work page 2021

  20. [20]

    Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,

    R. Ranftl, K. Lasinger, D. Hafner, K. Schindler, and V . Koltun, “Towards robust monocular depth estimation: Mixing datasets for zero-shot cross-dataset transfer,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 44, no. 3, pp. 1623–1637, 2022

    work page 2022

  21. [21]

    Vision transformers for dense prediction,

    R. Ranftl, A. Bochkovskiy, and V . Koltun, “Vision transformers for dense prediction,” inIEEE/CVF International Conference on Computer Vision (ICCV), pp. 12179–12188, 2021

    work page 2021

  22. [22]

    Pseudo-LiDAR from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving,

    Y . Wang, W.-L. Chao, D. Garg, B. Hariharan, M. Campbell, and K. Q. Weinberger, “Pseudo-LiDAR from visual depth estimation: Bridging the gap in 3D object detection for autonomous driving,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8445–8453, 2019

    work page 2019

  23. [23]

    Are we ready for autonomous driving? the KITTI vision benchmark suite,

    A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? the KITTI vision benchmark suite,” inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361, 2012

    work page 2012

  24. [24]

    3D bounding box estimation using deep learning and geometry,

    A. Mousavian, D. Anguelov, J. Flynn, and J. Košecká, “3D bounding box estimation using deep learning and geometry,” inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7074–7082, 2017

    work page 2017

  25. [25]

    Monocular 3D object detection for autonomous driving,

    X. Chen, K. Kundu, Z. Zhang, H. Ma, S. Fidler, and R. Urtasun, “Monocular 3D object detection for autonomous driving,” inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2147–2156, 2016

    work page 2016

  26. [26]

    M3D-RPN: Monocular 3D region proposal network for object detection,

    G. Brazil and X. Liu, “M3D-RPN: Monocular 3D region proposal network for object detection,” inIEEE/CVF International Conference on Computer Vision (ICCV), pp. 9287–9296, 2019

    work page 2019

  27. [27]

    Vision-based intelligent vehicles: State of the art and perspectives,

    M. Bertozzi, A. Broggi, and A. Fascioli, “Vision-based intelligent vehicles: State of the art and perspectives,” Robotics and Autonomous Systems, vol. 32, no. 1, pp. 1–16, 2000

    work page 2000

  28. [28]

    Vehicle detection techniques for collision avoidance systems: A review,

    A. Mukhtar, L. Xia, and T. B. Tang, “Vehicle detection techniques for collision avoidance systems: A review,” IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 5, pp. 2318–2338, 2015

    work page 2015

  29. [29]

    Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,

    S. Sivaraman and M. M. Trivedi, “Looking at vehicles on the road: A survey of vision-based vehicle detection, tracking, and behavior analysis,”IEEE Transactions on Intelligent Transportation Systems, vol. 14, no. 4, pp. 1773– 1795, 2013

    work page 2013

  30. [30]

    Automatic generation and detection of highly reliable fiducial markers under occlusion,

    S. Garrido-Jurado, R. Muñoz Salinas, F. J. Madrid-Cuevas, and M. J. Marín-Jiménez, “Automatic generation and detection of highly reliable fiducial markers under occlusion,”Pattern Recognition, vol. 47, no. 6, pp. 2280–2292, 2014

    work page 2014

  31. [31]

    AprilTag: A robust and flexible visual fiducial system,

    E. Olson, “AprilTag: A robust and flexible visual fiducial system,” inIEEE International Conference on Robotics and Automation (ICRA), pp. 3400–3407, 2011

    work page 2011

  32. [32]

    License plate recognition from still images and video sequences: A survey,

    C.-N. E. Anagnostopoulos, I. E. Anagnostopoulos, I. D. Psoroulas, V . Loumos, and E. Kayafas, “License plate recognition from still images and video sequences: A survey,”IEEE Transactions on Intelligent Transportation Systems, vol. 9, no. 3, pp. 377–391, 2008

    work page 2008

  33. [33]

    Automatic license plate recognition (ALPR): A state-of-the-art review,

    S. Du, M. Ibrahim, M. Shehata, and W. Badawy, “Automatic license plate recognition (ALPR): A state-of-the-art review,”IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 311–325, 2013

    work page 2013

  34. [34]

    Faster R-CNN: Towards real-time object detection with region proposal networks,

    S. Ren, K. He, R. Girshick, and J. Sun, “Faster R-CNN: Towards real-time object detection with region proposal networks,” inAdvances in Neural Information Processing Systems (NeurIPS), pp. 91–99, 2015

    work page 2015

  35. [35]

    SSD: Single shot MultiBox detector,

    W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y . Fu, and A. C. Berg, “SSD: Single shot MultiBox detector,” inEuropean Conference on Computer Vision (ECCV), pp. 21–37, 2016

    work page 2016

  36. [36]

    You only look once: Unified, real-time object detection,

    J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016

    work page 2016

  37. [37]

    YOLOv4: Optimal Speed and Accuracy of Object Detection

    A. Bochkovskiy, C.-Y . Wang, and H.-Y . M. Liao, “YOLOv4: Optimal speed and accuracy of object detection,” arXiv preprint arXiv:2004.10934, 2020

    work page internal anchor Pith review Pith/arXiv arXiv 2004

  38. [38]

    A robust real-time automatic license plate recognition based on the YOLO detector,

    R. Laroca, E. Severo, L. A. Zanlorensi, L. S. Oliveira, G. R. Gonçalves, W. R. Schwartz, and D. Menotti, “A robust real-time automatic license plate recognition based on the YOLO detector,” inInternational Joint Conference on Neural Networks (IJCNN), pp. 1–10, 2018

    work page 2018

  39. [39]

    Deep residual learning for image recognition,

    K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 770–778, 2016

    work page 2016

  40. [40]

    An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,

    B. Shi, X. Bai, and C. Yao, “An end-to-end trainable neural network for image-based sequence recognition and its application to scene text recognition,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 39, no. 11, pp. 2298–2304, 2017

    work page 2017

  41. [41]

    EasyOCR: Ready-to-use optical character recognition with 80+ supported languages

    JaidedAI, “EasyOCR: Ready-to-use optical character recognition with 80+ supported languages.” https:// github.com/JaidedAI/EasyOCR, 2020. 21 APREPRINT- JULY2, 2026

    work page 2020

  42. [42]

    An overview of the Tesseract OCR engine,

    R. Smith, “An overview of the Tesseract OCR engine,” inInternational Conference on Document Analysis and Recognition (ICDAR), vol. 2, pp. 629–633, 2007

    work page 2007

  43. [43]

    Towards end-to-end license plate detection and recognition: A large dataset and baseline,

    Z. Xu, W. Yang, A. Meng, N. Lu, H. Huang, C. Ying, and L. Huang, “Towards end-to-end license plate detection and recognition: A large dataset and baseline,” inEuropean Conference on Computer Vision (ECCV), pp. 255–271, 2018

    work page 2018

  44. [44]

    Application-oriented license plate recognition,

    G.-S. Hsu, J.-C. Chen, and Y .-Z. Chung, “Application-oriented license plate recognition,”IEEE Transactions on V ehicular Technology, vol. 62, no. 2, pp. 552–561, 2013

    work page 2013

  45. [45]

    The Cityscapes dataset for semantic urban scene understanding,

    M. Cordts, M. Omran, S. Ramos, T. Rehfeld, M. Enzweiler, R. Benenson, U. Franke, S. Roth, and B. Schiele, “The Cityscapes dataset for semantic urban scene understanding,” inIEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3213–3223, 2016

    work page 2016

  46. [46]

    nuScenes: A multimodal dataset for autonomous driving,

    H. Caesar, V . Bankiti, A. H. Lang, S. V ora, V . E. Liong, Q. Xu, A. Krishnan, Y . Pan, G. Baldan, and O. Beijbom, “nuScenes: A multimodal dataset for autonomous driving,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11621–11631, 2020

    work page 2020

  47. [47]

    Scalability in perception for autonomous driving: Waymo open dataset,

    P. Sun, H. Kretzschmar, X. Dotiwalla, A. Chouard, V . Patnaik, P. Tsui, J. Guo, Y . Zhou, Y . Chai, B. Caine,et al., “Scalability in perception for autonomous driving: Waymo open dataset,” inIEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2446–2454, 2020

    work page 2020

  48. [48]

    Hartley and A

    R. Hartley and A. Zisserman,Multiple View Geometry in Computer Vision. Cambridge University Press, 2nd ed., 2004

    work page 2004

  49. [49]

    EPnP: An accurate O(n) solution to the PnP problem,

    V . Lepetit, F. Moreno-Noguer, and P. Fua, “EPnP: An accurate O(n) solution to the PnP problem,”International Journal of Computer Vision, vol. 81, no. 2, pp. 155–166, 2009

    work page 2009

  50. [50]

    A consistently fast and globally optimal solution to the perspective-n-point problem,

    G. Terzakis and M. Lourakis, “A consistently fast and globally optimal solution to the perspective-n-point problem,” European Conference on Computer Vision (ECCV), pp. 478–494, 2020

    work page 2020

  51. [51]

    A flexible new technique for camera calibration,

    Z. Zhang, “A flexible new technique for camera calibration,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 11, pp. 1330–1334, 2000

    work page 2000

  52. [52]

    A new approach to linear filtering and prediction problems,

    R. E. Kalman, “A new approach to linear filtering and prediction problems,”Journal of Basic Engineering, vol. 82, no. 1, pp. 35–45, 1960

    work page 1960

  53. [53]

    An introduction to the Kalman filter,

    G. Welch and G. Bishop, “An introduction to the Kalman filter,” Tech. Rep. TR 95-041, University of North Carolina at Chapel Hill, Dept. of Computer Science, 1995

    work page 1995

  54. [54]

    Simple online and realtime tracking,

    A. Bewley, Z. Ge, L. Ott, F. Ramos, and B. Upcroft, “Simple online and realtime tracking,” inIEEE International Conference on Image Processing (ICIP), pp. 3464–3468, 2016

    work page 2016

  55. [55]

    Simple online and realtime tracking with a deep association metric,

    N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in IEEE International Conference on Image Processing (ICIP), pp. 3645–3649, 2017

    work page 2017

  56. [56]

    V2VNet: Vehicle-to-vehicle communication for joint perception and prediction,

    T.-H. Wang, S. Manivasagam, M. Liang, B. Yang, W. Zeng, and R. Urtasun, “V2VNet: Vehicle-to-vehicle communication for joint perception and prediction,” inEuropean Conference on Computer Vision (ECCV), pp. 605–621, 2020

    work page 2020

  57. [57]

    Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors,

    E. Arnold, M. Dianati, R. de Temple, and S. Fallah, “Cooperative perception for 3D object detection in driving scenarios using infrastructure sensors,”IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 3, pp. 1852–1864, 2022

    work page 2022

  58. [58]

    A threshold selection method from gray-level histograms,

    N. Otsu, “A threshold selection method from gray-level histograms,”IEEE Transactions on Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979

    work page 1979

  59. [59]

    A computational approach to edge detection,

    J. Canny, “A computational approach to edge detection,”IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. PAMI-8, no. 6, pp. 679–698, 1986

    work page 1986

  60. [60]

    Contrast limited adaptive histogram equalization,

    K. Zuiderveld, “Contrast limited adaptive histogram equalization,” inGraphics Gems IV, pp. 474–485, Academic Press, 1994

    work page 1994

  61. [61]

    The OpenCV library,

    G. Bradski, “The OpenCV library,”Dr . Dobb’s Journal of Software Tools, vol. 25, no. 11, pp. 120–125, 2000

    work page 2000

  62. [62]

    Topological structural analysis of digitized binary images by border following,

    S. Suzuki and K. Abe, “Topological structural analysis of digitized binary images by border following,”Computer Vision, Graphics, and Image Processing, vol. 30, no. 1, pp. 32–46, 1985

    work page 1985

  63. [63]

    Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,

    M. A. Fischler and R. C. Bolles, “Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,”Communications of the ACM, vol. 24, no. 6, pp. 381–395, 1981

    work page 1981

  64. [64]

    YOLOv3: An Incremental Improvement

    J. Redmon and A. Farhadi, “YOLOv3: An incremental improvement,”arXiv preprint arXiv:1804.02767, 2018. 22 APREPRINT- JULY2, 2026

    work page internal anchor Pith review Pith/arXiv arXiv 2018

  65. [65]

    Searching for MobileNetV3,

    A. Howard, M. Sandler, G. Chu, L.-C. Chen, B. Chen, M. Tan, W. Wang, Y . Zhu, R. Pang, V . Vasudevan, Q. V . Le, and H. Adam, “Searching for MobileNetV3,”IEEE/CVF International Conference on Computer Vision (ICCV), pp. 1314–1324, 2019

    work page 2019

  66. [66]

    Use of the Hough transformation to detect lines and curves in pictures,

    R. O. Duda and P. E. Hart, “Use of the Hough transformation to detect lines and curves in pictures,”Communica- tions of the ACM, vol. 15, no. 1, pp. 11–15, 1972. 10 Definitions, Acronyms, Abbreviations AAMV A American Association of Motor Vehicle Administrators CV Coefficient of variation (= 0.023) FHW A Federal Highway Administration FMVSS Federal Motor ...

    work page 1972