Vehicle Speed Measurement Based on Binocular Stereovision System

Yang, Lei; Li, Menglong; Song, Xiaowei; Xiong, Zixiang; Hou, Chunping; Qu, Boyang

doi:10.1109/access.2019.2932120

Cited by 29 publications

(75 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The flowchart is shown in Figure 1. In the SURF matching process, only feature points in the license plate regions of the left-view and right-view images are matched in [15]. Not only the number of matching calculations is reduced, but also the interference from the feature points outside the license plate regions is avoided.…”

Section: Proposed Methodsmentioning

confidence: 99%

An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

Yang

Song

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

This paper proposes an improved stereo matching algorithm for vehicle speed measurement system based on spatial and temporal image fusion (STIF). Firstly, the matching point pairs in the license plate area with obviously abnormal distance to the camera are roughly removed according to the characteristic of license plate specification. Secondly, more mismatching point pairs are finely removed according to local neighborhood consistency constraint (LNCC). Thirdly, the optimum speed measurement point pairs are selected for successive stereo frame pairs by STIF of binocular stereo video, so that the 3D points corresponding to the matching point pairs for speed measurement in the successive stereo frame pairs are in the same position on the real vehicle, which can significantly improve the vehicle speed measurement accuracy. LNCC and STIF can be used not only for license plate, but also for vehicle logo, light, mirror etc. Experimental results demonstrate that the vehicle speed measurement system with the proposed LNCC+STIF stereo matching algorithm can significantly outperform the state-of-the-art system in accuracy.

show abstract

Section: Proposed Methodsmentioning

confidence: 99%

An Improved Stereo Matching Algorithm for Vehicle Speed Measurement System Based on Spatial and Temporal Image Fusion

Yang

Song

et al. 2021

Entropy

Self Cite

View full text Add to dashboard Cite

show abstract

“…Yang et al [12] use a horizontal stereo setup calibrated using Zhang's method [10]. They detect license plates using a single shot multibox detector.…”

Section: Related Workmentioning

confidence: 99%

“…The mean absolute percentage error is 0.23 % and maximum percentage error is 1.11 %. We compare the speed measurement errors with three other stereo-based vehicle speed measurement methods and one two-camera method mentioned in chapter II, namely, Jalalat et al's method [9], El Bouziady et al's method [11], Yang et al's method [12], and Llorca et al's [14] method. The comparison is shown in Table II.…”

Section: E Speed Measurementmentioning

confidence: 99%

Vehicle Speed Measurement Using Stereo Camera Pair

Najman

Zemčík

2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

“…In the last decade, the 3D vision technique has become an intensive research topic in the various application field, such as human external shape reconstruction [8], aerospace, medical field [9], landslide mapping [10] and so on. Image registration is of great importance for the coordination computation based on binocular vision [11, 12]. Generally, the main research methods can be divided into two classes: feature‐based and intensity‐based methods [13].…”

Section: Error Distributionmentioning

confidence: 99%