Abstract
In order to reduce road congestion, measures such as setting smart signal time schedules are needed. Therefore, it is a key technology to effectively and accurately count the traffic flow of vehicles at various intersections in the surveillance video. The method we propose uses Scaled-YOLOv4 as the vehicle detector, and then implements the vehicle tracking based on the DEEP SORT algorithm. To improve the accuracy and efficiency of the system, we propose a strategy of dynamic frame skipping based on density. We also propose to set key areas, combined with the driving direction, angle, etc., to judge and count the behavior of the vehicle. Experiments show that our method improved system efficiency while remaining high accuracy.
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Acknowledgements
The work is partially supported by the National Key Research and Development Program of China (2020YFB1707900), and Liaoning Distinguished Professor (No. XLYC1902057).
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Qi, R., Liu, Y., Zhang, Z., Yang, X., Wang, G., Jiang, Y. (2022). Fast Vehicle Track Counting in Traffic Video. In: Rage, U.K., Goyal, V., Reddy, P.K. (eds) Database Systems for Advanced Applications. DASFAA 2022 International Workshops. DASFAA 2022. Lecture Notes in Computer Science, vol 13248. Springer, Cham. https://doi.org/10.1007/978-3-031-11217-1_18
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