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Exploring Deep Learning in Road Traffic Accident Recognition for Roadside Sensing Technologies

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Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1 (FTC 2022 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 559))

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Abstract

Road traffic accident recognition is essential in providing timely information to healthcare authorities for reducing fatalities. This area of research is heavily dependent on traffic flow data captured through roadside sensing technologies that are installed on highways and intersections. To date, Deep Learning (DL) has achieved remarkable progress in solving time-series problems with increasing applications in road traffic accident recognition. This paper explores recent studies of DL techniques using roadside sensor-based traffic flow data. Limited literature has focused on road traffic accident recognition in mixed traffic. Various issues in current DL recognition solutions that affect accuracy, including consideration of user varieties, dynamic traffic flow conditions, and external environmental factors are discussed. In this research, a fusion feature-based deep learning model for traffic accident recognition has been proposed, consisting of three major streams of models to cater for prominent features in traffic accident recognition in a mixed traffic flow environment.

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Authors and Affiliations

  1. Swinburne University of Technology Sarawak Campus, Kuching, Sarawak, Malaysia

    Swee Tee Fu, Bee Theng Lau, Mark Kit Tsun Tee & Brian Chung Shiong Loh

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  1. Swee Tee Fu
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  2. Bee Theng Lau
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Correspondence to Swee Tee Fu .

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  1. Faculty of Science and Engineering, Saga University, Saga, Japan

    Kohei Arai

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Fu, S.T., Lau, B.T., Tee, M.K.T., Loh, B.C.S. (2023). Exploring Deep Learning in Road Traffic Accident Recognition for Roadside Sensing Technologies. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 559. Springer, Cham. https://doi.org/10.1007/978-3-031-18461-1_3

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