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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References
Aich, S., Stavness, I.: Leaf counting with deep convolutional and deconvolutional networks. arXiv preprint arXiv:1708.07570 (2017)
Dai, Z., Song, H., Wang, X., Fang, Y., Li, H.: Video-based vehicle counting framework. IEEE Access 99, 1–1 (2019)
Embleton, K.V., Gibson, C.E., Heaney, S.I.: Automated counting of phytoplankton by pattern recognition: a comparison with a manual counting method. J. Plankton Res. 25(6), 669–681 (2003)
Hsieh, K., et al.: Focus: querying large video datasets with low latency and low cost. In: 13th \(\{\)USENIX\(\}\) Symposium on Operating Systems Design and Implementation (\(\{\)OSDI\(\}\) 18), pp. 269–286 (2018)
Kang, D., Emmons, J., Abuzaid, F., Bailis, P., Zaharia, M.: NoScope: optimizing deep CNN-based queries over video streams at scale. Proc. VLDB Endow. 10(11), 1586–1597 (2017)
Kang, D., Guibas, J., Bailis, P., Hashimoto, T., Zaharia, M.: Task-agnostic indexes for deep learning-based queries over unstructured data. arXiv preprint arXiv:2009.04540 (2020)
Liang, H., Song, H., Li, H., Dai, Z.: Vehicle counting system using deep learning and multiobject tracking methods. Transpo. Res. Record. 2674(4), 114–128 (2020)
Naphade, M., et al.: The 5th AI city challenge. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, June 2021
Pizzo, L.D., Foggia, P., Greco, A., Percannella, G., Vento, M.: Counting people by RGB or depth overhead cameras. Patt. Recogn. Lett. 81(C), 41–50 (2016)
Shih, F.Y., Zhong, X.: Automated counting and tracking of vehicles. Int. J. Pattern Recognit. Artif. Intell . 31(12), 1750038.1–1750038.12 (2017)
Tran, V.H., et al.: Real-time and robust system for counting movement-specific vehicle at crowded intersections. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 4228–4235 (2021)
Wang, C.Y., Bochkovskiy, A., Liao, H.Y.M.: Scaled-yolov4: Scaling cross stage partial network. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 13029–13038 (2021)
Wojke, N., Bewley, A., Paulus, D.: Simple online and realtime tracking with a deep association metric. In: 2017 IEEE international conference on image processing (ICIP), pp. 3645–3649. IEEE (2017)
Yuan, H., Li, G.: A survey of traffic prediction: from spatio-temporal data to intelligent transportation. Data Sci. Eng. 6(1), 63–85 (2021)
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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