Proposal a Vehicle Speed Measuring System Using Image Processing

Goda, Yuji; Zhang, Lifeng; Serikawa, Seiichi

doi:10.1109/is3c.2014.146

Cited by 7 publications

(1 citation statement)

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“…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

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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.

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Section: A Cnn Architecture That Measures the Space Mean Speedmentioning

confidence: 99%