Abstract
Pedestrian detection technology is applied to more and more scenes, which shows high application value. In recent years, with the development of electronic information technology, the computing speed of computers has been growing rapidly, and the deep learning technology has become better and better with the development of computers. In this paper, based on YOLOv4, this paper studied the scheme of pedestrian detection, obtained the anchor of the pedestrian data through the K-Means algorithm, the loss function of the target detection algorithm is optimized, and introduced the Soft-NMS to improve the pedestrian occlusion problem in detection. Through relevant model verification experiments, the algorithm in this paper is faster than the traditional target detection algorithm in terms of speed, accuracy and robustness.
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Acknowledgement
This work is supported by the Shandong Key Technology R&D Program 2019JZZY021005, Natural Science Foundation of Shandong ZR2020MF067, ZR2021MF074 and ZR2022MF296.
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Zhao, Q., Du, K., Yu, H., Hu, S., Jing, R., Wen, X. (2024). Algorithm of Pedestrian Detection Based on YOLOv4. In: Wang, B., Hu, Z., Jiang, X., Zhang, YD. (eds) Multimedia Technology and Enhanced Learning. ICMTEL 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-031-50580-5_19
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DOI: https://doi.org/10.1007/978-3-031-50580-5_19
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