Evidence map›Paper›PMID 39843469›Full record

ArticleScientific reports2025

Vehicle speed measurement method using monocular cameras.

Hao Lian, Meian Li, Ting Li, Yongan Zhang, Yanyu Shi, Yikun Fan, Wenqian Yang, Huilin Jiang, Peng Zhou, Haibo Wu

Erratum issuedAbstract read
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In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Interpol review of forensic image and video analysis, 2022-2025.Forensic science international. Synergy · 2026
    Review
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Hao LianComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Meian LiComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China. limeian1973@126.com.
Ting LiComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Yongan ZhangComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Yanyu ShiComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Yikun FanComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Wenqian YangComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Huilin JiangComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Peng ZhouComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.
Haibo WuComputer and Information Engineering College, Inner Mongolia Agricultural University, Hohhot, 010000, China.

Funding

Research and Demonstration of Key Technologies for Multi scale Yellow River Ice Situation Automatic Detection Based on AIoT BR231407Research on Feature Point Space Invariance Method for Multi target Individual Identity Recognition of Livestock 2023LHMS06012
6 · The paper itself

Abstract

This paper proposes a method for fast and accurate vehicle speed measurement based on a monocular camera. Firstly, by establishing a new camera imaging model, the calibration method for variable focal lengths is optimized, simplifying the transformation process between the four coordinate systems in traditional camera imaging models, and the method does not need to restore the pixel coordinates to dedistortion. Secondly, based on the camera imaging model, a two-dimensional positioning algorithm is proposed. By leveraging the characteristics of the speed measurement problem, the complex three-dimensional positioning problem is simplified into a two-dimensional model, reducing the overall computational complexity of the positioning problem. Finally, the algorithm is combined with You Only Look Once version 7 (YOLOv7) and Deep Simple Online and Realtime Tracking (DeepSORT) algorithms, integrating multiple model structures to optimize the network, achieving precise multi-target speed measurement. Experiments show that under frame-by-frame measurement conditions, the minimum and average accuracies of this method reach 95.1% and 97.6%, respectively. Compared with other methods, it has significant advantages in speed measurement accuracy and computational efficiency. Therefore, this research outcome is expected to play an important role in intelligent transportation systems and road safety management.

Indexed as

Camera calibrationDeepSORTMonocular visionTwo-dimensional positioningYOLOv7

Identifiers

PMID39843469
PMCPMC11754901

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.