Abstract
In the context of the current normalization of epidemic prevention, the nucleic acid detection process in colleges and universities is limited in human and material resources. Teachers and students who perform nucleic acid detection often cannot maintain a distance of more than one meter from others, and there is a pedestrian group behavior that has a large cross-infection safety hazard. This article uses Depthwise Separable Convolution to improve the YOLOv3 algorithm, and the improved network structure constructs a pedestrian detection, pedestrian tracking, pedestrian counting and pedestrian cluster system based on Deep Learning under the TensorFlow framework. The training parameters and training time of the improved network model are reduced to a certain extent, improved the operation efficiency of the network model. The advantage is that it realizes the function of monitoring centralized nucleic acid detection scenes in colleges and universities and assisting volunteers to maintain a reasonable order, which can effectively prevent cross-infection problems caused by cluster effects.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Du, P., Chen, M., Su, T.: Deep Learning and Target Detection. Electronic Industry Press, Beijing, pp. 2–25 (2019)
Pooja, G., Varsha, S., Sunita, V.: People detection and counting using YOLOv3 and SSD models. Mater. Today. Proc. 44, 2069–2079 (2021)
Li, L., Guo, B., Shao, K.: Geometrically robust image watermarking using scale-invariant feature transform and Zernike moments. Chin. Optics Lett. 06, 332–335 (2007)
Zhu, Q., Yeh, M.C., Cheng, K.T.: Fast human detection using a cascade of histograms of oriented gradients. In: 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 1491–149. IEEE (2006)
Girshick, R.: Fast R-CNN. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV), pp. 1440–1448 (2015)
Hubel, D.H., Wiesel, T.N.: Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol. 160(1), 106–154 (1962)
Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN.: towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (NIPS), pp. 91–99 (2015)
Redmon, J., et al.: You only look once: unified, real-time object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, pp. 779–788 (2016)
Cao, S., Zhao, D., Liud, X.: Real-time robust detector for underwater live crabs based on deep learning. Comput. Electron. Agric. 172, 105339 (2020)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once.: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 779–788 (2016)
Redmon, J., Farhadi, A.: YOLOv3: an incremental improvement. arXiv preprint: arXiv:1804.02767 (2018)
Liu, W., et al.: SSD: single shot multibox detector. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 21–37. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_2
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Du, R., Zhao, J., Xie, J., Wen, T. (2022). Pedestrian Detection Based on Deep Learning Under the Background of University Epidemic Prevention. In: Jin, H., Liu, C., Pathan, AS.K., Fadlullah, Z.M., Choudhury, S. (eds) Cognitive Radio Oriented Wireless Networks and Wireless Internet. CROWNCOM WiCON 2021 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 427. Springer, Cham. https://doi.org/10.1007/978-3-030-98002-3_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-98002-3_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-98001-6
Online ISBN: 978-3-030-98002-3
eBook Packages: Computer ScienceComputer Science (R0)