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Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density

Laboratory of Big-Data Applications in Public Sectors, Department of Urban Engineering, Chung-Ang University, 84 Heukseok-ro, Dongjak-gu, Seoul 156-756, Korea
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Academic Editor: Stefanos Kollias
Electronics 2021, 10(10), 1189; https://doi.org/10.3390/electronics10101189
Received: 28 April 2021 / Revised: 11 May 2021 / Accepted: 14 May 2021 / Published: 16 May 2021
(This article belongs to the Special Issue AI-Based Transportation Planning and Operation, Volume II)
Whereas detecting individual vehicles in a video image using a convolutional neural network (CNN) prevails for traffic surveillance, CNNs also have been successfully adapted to counting vehicles via a regression method, which conveys the advantages of simplifying the model structure, and inference time can be reduced in the field. This model also demands much less human effort to tag images with labels. The number of vehicles in an image becomes the label, rather than bounding boxes drawn around every single vehicle. Nonetheless, the labeling task takes considerable time whenever a CNN model is trained and tested for a new road segment. There are two ways to alleviate the human effort involved in using this method. A previous study used a pseudo label pre-training method, and another study employed an image synthesis method to solve the problem. Besides these two methods, we investigated the model transferability to reduce the labeling effort. Using a CNN that was fully trained on images of a road segment, we devised a robust way to utilize the trained model for another site by transforming the model output with a simple quadratic equation. The utility of the proposed method was confirmed at the expense of a minute amount of deterioration in accuracy. View Full-Text
Keywords: model transferability; traffic surveillance; convolutional neural network; traffic density model transferability; traffic surveillance; convolutional neural network; traffic density
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MDPI and ACS Style

Chung, J.; Kim, G.; Sohn, K. Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density. Electronics 2021, 10, 1189. https://doi.org/10.3390/electronics10101189

AMA Style

Chung J, Kim G, Sohn K. Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density. Electronics. 2021; 10(10):1189. https://doi.org/10.3390/electronics10101189

Chicago/Turabian Style

Chung, Jiyong, Gyeongjun Kim, and Keemin Sohn. 2021. "Transferability of a Convolutional Neural Network (CNN) to Measure Traffic Density" Electronics 10, no. 10: 1189. https://doi.org/10.3390/electronics10101189

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