分割可跨越车道分界线的多视角视频车速提取方法

doi:10.13229/j.cnki.jdxbgxb.20230818

Abstract

Abstract:

Aiming at the problem that the existing video traffic parameter extraction method relies too much on manual labeling and the single perspective cannot effectively correct the dynamic driving deviation of on-site vehicles， a multi-view video traffic parameter extraction method that can split the lane demarcation line is proposed. This method consists of an automatic generation module for labeling points and a module for multi-view correction. The automatic generation of label points module realizes the process of automatically generating label points by constructing a reference block based on the dividing line of equal-length lanes. The multi-view deviation correction module proposes a variety of mapping methods for the boundary between vehicles and lanes and a correction speed measurement method based on the average speed probability density function to correct two types of deviations generated by vehicles when driving dynamically. The experimental results on the public dataset and the measured dataset show that the speed extraction accuracy of the proposed method is better than that of other speed measurement methods， and has certain universality.

Key words: computer application, intelligent transportation, lane demarcation line, multiple perspectives, vehicle speed detection, computer vision

CLC Number:

TP391

Yue HOU,Jin-song GUO,Wei LIN,Di ZHANG,Yue WU,Xin ZHANG. Multi-view video speed extraction method that can be segmented across lane demarcation lines[J].Journal of Jilin University(Engineering and Technology Edition), 2025, 55(5): 1692-1704.

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实验环境	配置
操作系统	Windows 11
处理器	AMD Ryzen 7 5800H with Radeon Graphics
内存	16.0 GB
编程语言	Python

分割次数	第一次抽样		第二次抽样		第三次抽样
分割次数	平均误差率	最大误差率	平均误差率	最大误差率	平均误差率	最大误差率
r=0	1.64	2.75	1.75	2.87	1.34	2.23
r=1	1.53	2.56	1.45	2.42	1.27	1.94
r=2	1.35	2.38	1.33	2.46	0.97	1.94
r=3	1.18	2.34	1.06	2.28	0.91	1.71

映射方式	正视角		侧视角		模糊视角
映射方式	平均误差率	最大误差率	平均误差率	最大误差率	平均误差率	最大误差率
LL-TR	1.06	2.28	1.71	2.84	1.23	2.47
LL-PA	2.17	3.37	1.18	2.34	2.14	3.30
LL-MX	1.75	2.47	1.48	2.34	0.91	1.71
LL-N	2.71	3.53	2.73	3.28	3.45	4.53

组别	正视角		侧视角		模糊视角
组别	平均误差率	最大误差率	平均误差率	最大误差率	平均误差率	最大误差率
对照组	2.43	3.96	2.19	3.37	1.82	2.52
实验组	1.18	2.34	1.06	2.28	0.91	1.71

方法	平均误差率
方法	BrnoCompSpeed	LzVedioSpeed
GPS	2.18	—
RADAR	1.33	—
T-LL	1.71	1.78
M-LL	1.40	1.42
Vcm	3.06	3.02
video-speed	1.84	4.21
Vcm+centroid	5.43	2.89
本文	1.12	1.16

Multi-view video speed extraction method that can be segmented across lane demarcation lines

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