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http://dx.doi.org/10.6109/jkiice.2021.25.1.146

Analysis of Deep Learning Model for the Development of an Optimized Vehicle Occupancy Detection System  

Lee, JiWon (Department of Computer Engineering, Dong-Eui University)
Lee, DongJin (Department of Computer Engineering, Dong-Eui University)
Jang, SungJin (Department of Computer Engineering, Dong-Eui University)
Choi, DongGyu (Department of Computer Engineering, Dong-Eui University)
Jang, JongWook (Department of Computer Engineering, Dong-Eui University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.1, 2021 , pp. 146-151 More about this Journal
Abstract
Currently, the demand for vehicles from one family is increasing in many countries at home and abroad, reducing the number of people on the vehicle and increasing the number of vehicles on the road. The multi-passenger lane system, which is available to solve the problem of traffic congestion, is being implemented. The system allows police to monitor fast-moving vehicles with their own eyes to crack down on illegal vehicles, which is less accurate and accompanied by the risk of accidents. To address these problems, applying deep learning object recognition techniques using images from road sites will solve the aforementioned problems. Therefore, in this paper, we compare and analyze the performance of existing deep learning models, select a deep learning model that can identify real-time vehicle occupants through video, and propose a vehicle occupancy detection algorithm that complements the object-ident model's problems.
Keywords
Object detection; HOV lane; Deep learning; Custom dataset;
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