Abstract
Aiming to address the problem of counting multi-target moving vehicles in the various complex traffic environments, this paper proposes a detecting and tracking method based on YOLO (You Only Look Once) and Deep Sort, and evaluates its performance with public dataset (TUA-DETRAC) and two self-collection datasets. The YOLOv4 algorithm is firstly used to detect each moving vehicles, and then Deep Sort algorithm is adopted to track multi-target vehicles. The experimental results show that moving vehicles can be effectively detected and tracked in real time under different traffic environments including daytime, nighttime, rainy and crowded scenes. The experimental results show that the proposed method can reach 93% average detection accuracy with 20fps of tracking speed, and is capable of dealing with different traffic and climate conditions.
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Acknowledgements
We acknowledge funding from the Hainan Provincial Natural Science Foundation of China (No: 620RC558), Natural Science Foundation Project of CQCSTC (No. cstc2018jcyj AX0398), Science Project of Hainan University (KYQD(ZR)20022).
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Yan, J., Wang, Z., Li, Y., Chen, Z., Chen, X., Lou, L. (2022). Detecting and Tracking the Moving Vehicles Based on Deep Learning. In: Jansen, T., Jensen, R., Mac Parthaláin, N., Lin, CM. (eds) Advances in Computational Intelligence Systems. UKCI 2021. Advances in Intelligent Systems and Computing, vol 1409. Springer, Cham. https://doi.org/10.1007/978-3-030-87094-2_32
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DOI: https://doi.org/10.1007/978-3-030-87094-2_32
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