A Novel Motion Plane-Based Approach to Vehicle Speed Estimation

Famouri, Mahmoud; Azimifar, Zohreh; Wong, Alexander

doi:10.1109/tits.2018.2847224

Cited by 22 publications

(30 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Famouri et al [19] refined the approach of Luvizon et al [18] by measuring the distances on actual vehicle motion plane by calculating the height of the license plate above the road plane using the Shape-from-template method [20]. By doing that, they reduced the mean error of Luvizon et al's [18] method by 25 %.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Speed Measurement Using Stereo Camera Pair

Najman

Zemčík

2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

Section: Related Workmentioning

confidence: 99%

Vehicle Speed Measurement Using Stereo Camera Pair

Najman

Zemčík

2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

“…It is a rational assumption that the space mean speed could be estimated by scaling these changes. Many researchers have devised various engineering tools to measure vehicle speeds based on this assumption [ 28 , 29 , 30 ]. However, these engineering-based models were more complex and less transferable than an end-to-end learning model.…”

Section: A Cnn Architecture That Measures the Space Mean Speedmentioning

confidence: 99%

Image-Based Learning to Measure the Space Mean Speed on a Stretch of Road without the Need to Tag Images with Labels

Lee

Roh

Shin

et al. 2019

Sensors

View full text Add to dashboard Cite

Space mean speed cannot be directly measured in the field, although it is a basic parameter that is used to evaluate traffic conditions. An end-to-end convolutional neural network (CNN) was adopted to measure the space mean speed based solely on two consecutive road images. However, tagging images with labels (=true space mean speeds) by manually positioning and tracking every vehicle on road images is a formidable task. The present study was focused on naïve animation images provided by a traffic simulator, because these contain perfect information concerning vehicle movement to attain labels. The animation images, however, seem far-removed from actual photos taken in the field. A cycle-consistent adversarial network (CycleGAN) bridged the reality gap by mapping the animation images into seemingly realistic images that could not be distinguished from real photos. A CNN model trained on the synthesized images was tested on real photos that had been manually labeled. The test performance was comparable to those of state-of-the-art motion-capture technologies. The proposed method showed that deep-learning models to measure the space mean speed could be trained without the need for time-consuming manual annotation.

show abstract

“…However, these devices are easy to be detected due to the transmitted signals, which is undesirable for secret measurement. Video-based speed measurement has gained more and more attention because of its low cost, easy concealment and convenient combination of vehicle speed and vehicle information [ 11 , 12 , 13 , 14 , 15 ]. According to the video acquisition way, video-based methods can be further divided into two main categories: two-dimensional (2D) video-based method and three-dimensional (3D) video-based method.…”

Section: Introductionmentioning

confidence: 99%

“…The methods in [ 11 , 12 ] belong to 2D video-based speed measurement method. A vehicle speed measurement method based on pinhole imaging projection model combining frame difference with edge detection is proposed in [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

“…A vehicle speed measurement method based on pinhole imaging projection model combining frame difference with edge detection is proposed in [ 11 ]. The method in [ 12 ] is an improved version of the method in [ 11 ], which uses the shape-from-template technology to make the projection model more accurate and further improve the speed measurement accuracy. Nevertheless, the methods in [ 11 , 12 ] both utilize the principle of pinhole imaging, which is only suitable for speed measurement scenarios with vehicle traveling in a straight line.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Yang

Song

et al. 2021

Entropy

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

A Novel Motion Plane-Based Approach to Vehicle Speed Estimation

Cited by 22 publications

References 16 publications

Vehicle Speed Measurement Using Stereo Camera Pair

Vehicle Speed Measurement Using Stereo Camera Pair

Image-Based Learning to Measure the Space Mean Speed on a Stretch of Road without the Need to Tag Images with Labels

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

Contact Info

Product

Resources

About