Abstract
Traffic flow analysis in complex areas (e.g., intersections and roundabouts) plays an important part in the development of intelligent transportation systems. Among several methods for analyzing traffic flow, image and video processing has emerged as a potential approach to extract the movements of vehicles in urban areas. In this regard, this study develops a traffic flow analysis method, which focuses on extracting traffic information based on Video Surveillance (CCTV) for turning volume estimation at complex intersections, using advanced computer vision technologies. Specifically, state-of-the-art techniques such as Yolo and DeepSORT for the detection, tracking, and counting of vehicles have enveloped to estimate the road traffic density. Regarding the experiment, we collected data from CCTV in an urban area during one day to evaluate our method. The evaluation shows the proposing results in terms of detecting, tracking and counting vehicles with monocular videos.
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Acknowledgment
This work was partly supported by Institute for Information & communications Technology Promotion (IITP) grant funded by the Korea government (MSIT) (No. 2018-0-00494, Development of deep learning-based urban traffic congestion prediction and signal control solution system) and Korea Institute of Science and Technology Information (KISTI) grant funded by the Korea government (MSIT) (K-19-L02-C07-S01).
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Bui, KH.N., Yi, H., Jung, H., Cho, J. (2020). Video-Based Traffic Flow Analysis for Turning Volume Estimation at Signalized Intersections. In: Nguyen, N., Jearanaitanakij, K., Selamat, A., Trawiński, B., Chittayasothorn, S. (eds) Intelligent Information and Database Systems. ACIIDS 2020. Lecture Notes in Computer Science(), vol 12034. Springer, Cham. https://doi.org/10.1007/978-3-030-42058-1_13
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