Work place: Multimedia Data Analytics and Processing Research Unit, Department of Electrical Engineering, Chulalongkorn University, Bangkok, 10330, Thailand
Website: Supavadee Aramith
Research Interests: Electrical Engineering, Engineering, Computational Engineering, Electronic Engineering
Supavadee Aramvith (Senior Member, IEEE) received the B.S. degree (Hons.) in computer science from Mahidol University, in 1993, and the M.S. and Ph.D. degrees in electrical engineering from the University of Washington, Seattle, USA, in 1996 and 2001, respectively. In June 2001, she joined Chulalongkorn University, where she is currently an Associate Professor at the Department of Electrical Engineering, with a specialization in video technology. She has successfully advised 32 bachelor’s, 27 master’s, and nine Ph.D. graduates. She published over 130 articles in international conference proceedings and journals with four international book chapters. She has rich project management experiences as a project leader and a former technical committee chair to the Thailand Government bodies in Telecommunications and ICT. She is very active in the international arena with the leadership positions in the international network, such as JICA Project for AUN/SEED-Net, and the professional organizations, such as the IEEE, IEICE, APSIPA, and ITU. She is currently a member of the IEEE Educational Activities Board (EAB) and the Chair of the IEEE EAB Pre-University Education Coordination Committee. She is also a member of the Board of Governors of the IEEE Consumer Electronics Society, from 2019 to 2021. She formerly led Educational Activities and Women in Engineering for the IEEE Asia Pacific (Region 10), from 2011 to 2016.
DOI: https://doi.org/10.5815/ijigsp.2022.06.06, Pub. Date: 8 Dec. 2022
Deep learning (DL) architectures are becoming increasingly popular in modern traffic systems and self-driven vehicles owing to their high efficiency and accuracy. Emerging technological advancements and the availability of large databases have made a favorable impact on such improvements. In this study, we present a traffic sign recognition system based on novel DL architectures, trained and tested on a locally collected traffic sign database. Our approach includes two stages; traffic sign identification from live video feed, and classification of each sign. The sign identification model was implemented with YOLO architecture and the classification model was implemented with Xception architecture. The input video feed for these models were collected using dashboard camera recordings. The classification model has been trained with the German Traffic Sign Recognition Benchmark dataset as well for comparison. Final accuracy of classification for the local dataset was 96.05% while the standard dataset has given an accuracy of 92.11%. The final model is a combination of the detection and classification algorithms and it is able to successfully detect and classify traffic signs from an input video feed within an average detection time of 4.5fps[...] Read more.
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