Abstract
With the continuous development of science and technology, more and more fields are applying emerging video surveillance technology to ensure public safety. Compared with the way of manually processing video, the video monitoring technology based on deep learning also has the advantages of higher efficiency, high accuracy and intelligence when processing more and more massive video image data. In this paper, aiming at the detection requirements of personnel intrusion in the perimeter of high-speed railways, it realizes cross-region tracking of suspicious persons. It studies the application of deep learning cross-region tracking algorithm based on ResNet-50 in high-speed railway perimeter intrusion detection, and analyzes its reliability and optimizes accuracy. The degree reached 88%. Compared with the results of other cross-region tracking algorithms, the detection accuracy is still high. Therefore, it can be seen that the high-speed rail perimeter cross-region target tracking based on deep learning is feasible and efficient.
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Acknowledgment
The work was supported in part by the National Key R&D Program of China under Grant (No. 2020YFF0304104).
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Guo, T., Jia, L., Xie, Z., Qin, Y., Lin, F. (2022). Research on the Algorithm of Cross-region Target Tracking in the Perimeter of High Speed Railway Based on Deep Learning. In: Liang, J., Jia, L., Qin, Y., Liu, Z., Diao, L., An, M. (eds) Proceedings of the 5th International Conference on Electrical Engineering and Information Technologies for Rail Transportation (EITRT) 2021. EITRT 2021. Lecture Notes in Electrical Engineering, vol 867. Springer, Singapore. https://doi.org/10.1007/978-981-16-9909-2_19
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DOI: https://doi.org/10.1007/978-981-16-9909-2_19
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