%0 Journal Article %T Image-based learning to measure the space mean speed on a stretch of road without the need to tag images with labels %J Sensors (Basel) %D 2019 %A Lee, Jincheol %A Roh, Seungbin %A Shin, Johyun %A Sohn, Keemin %V 19 %N 5 %P s19051227-s19051227 %X 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.

Language: en

%G en %I MDPI: Multidisciplinary Digital Publishing Institute %@ 1424-8220 %U http://dx.doi.org/10.3390/s19051227