Yang Huang
ABSTRACT
In oil field operations, accidents often occur, with personnel error, improper behavior, and mechanical failures accounting for a significant proportion. To improve safety levels, pedestrian re-identification technology has been widely used. However, the complex nature of oil field operations leads to traditional technologies having high error and missing identification rates. Field adaptive pedestrian re-identification technology for oil field safety uses deep learning algorithms to self-adapt and learn, improving accuracy and stability to enable monitoring and management of personnel behavior for safety in oil field operations.
- Lei Shi. (2021). Qiantan youtian xinxi jishu anfang jiankong xitong zai minyong shichang de kaifa ji yingyong [Introduction to the development and application of oilfield information technology security monitoring system in the civil market]. China Plant Engineering (16),200-201.Google Scholar
- Yang Liu.(2019). Wuxian wangluo jishu zai youtian anfang jiankong xitong zhongde yingyong yanjiu [Research on the application of wireless network technology in oilfield security monitoring system]. China Plant Engineering (20),227-229.Google Scholar
- Liming Feng,Shuhuo Wu& Yong Zhuo.(2020). Youqi guandao anquan fanghu zhineng shipin jiankong xitong sheji [Design of intelligent video monitoring system for oil and gas pipeline safety protection]. Technology Supervision in Petroleum Industry (08),48-51+60.Google Scholar
- Aich, A., Zheng, M., Karanam, S., Chen, T., Roy-Chowdhury, A. K., & Wu, Z. (2021). Spatio-temporal representation factorization for video-based person re-identification. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 152-162).Google ScholarCross Ref
- Jingjing LI, Lichao MENG, Ke ZHANG, Ke LU, Hengtao SHEN. (2021). Review of Studies on Domain Adaptation[J]. Computer Engineering, 47(6): 1-13.Google Scholar
- Ge-ge WANG, Tao GUO, You YU, Han SU. (2020) Unsupervised Domain Adaptation Classification Model Based on Generative Adversarial Network[J]. ACTA ELECTONICA SINICA, 48(6): 1190-1197 https://doi.org/10.3969/j.issn.0372-2112.2020.06.021.Google ScholarCross Ref
- Chicco, D. (2021). Siamese neural networks: An overview. Artificial neural networks, 73-94.Google Scholar
- Hoffer, E., & Ailon, N. (2015). Deep metric learning using triplet network. In Similarity-Based Pattern Recognition: Third International Workshop, SIMBAD 2015, Copenhagen, Denmark, October 12-14, 2015. Proceedings 3 (pp. 84-92). Springer International Publishing.Google ScholarCross Ref
- Oh, H., Ahn, S., Kim, J., & Lee, S. (2017). Blind deep S3D image quality evaluation via local to global feature aggregation. IEEE Transactions on Image Processing, 26(10), 4923-4936.Google ScholarDigital Library
- Smola, A. J., Gretton, A., & Borgwardt, K. (2006). Maximum mean discrepancy. In 13th international conference, ICONIP (pp. 3-6).Google Scholar
- Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., & Lempitsky, V. (2016). Domain-adversarial training of neural networks. The journal of machine learning research, 17(1), 2096-2030.Google Scholar
- Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019). An empirical study of spatial attention mechanisms in deep networks. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 6688-6697).Google ScholarCross Ref
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.Google Scholar
- Hashiguchi, R., & Tamaki, T. (2022). Vision transformer with cross-attention by temporal shift for efficient action recognition. arXiv preprint arXiv:2204.00452.Google Scholar
- Tzeng, E., Hoffman, J., Saenko, K., & Darrell, T. (2017). Adversarial discriminative domain adaptation. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7167-7176).Google ScholarCross Ref
- Zhu, K., Guo, H., Zhang, S., Wang, Y., Liu, J., Wang, J., & Tang, M. (2023). Aaformer: Auto-aligned transformer for person re-identification. IEEE Transactions on Neural Networks and Learning Systems.Google Scholar
- Hirzer, M., Beleznai, C., Roth, P. M., & Bischof, H. (2011). Person re-identification by descriptive and discriminative classification. In Image Analysis: 17th Scandinavian Conference, SCIA 2011, Ystad, Sweden, May 2011. Proceedings 17 (pp. 91-102). Springer Berlin Heidelberg.Google ScholarCross Ref
- Felzenszwalb, P. F., Girshick, R. B., McAllester, D., & Ramanan, D. (2009). Object detection with discriminatively trained part-based models. IEEE transactions on pattern analysis and machine intelligence, 32(9), 1627-1645.Google Scholar
- Dehghan, A., Modiri Assari, S., & Shah, M. (2015). Gmmcp tracker: Globally optimal generalized maximum multi clique problem for multiple object tracking. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4091-4099).Google ScholarCross Ref
- Zhao, Y., Shen, X., Jin, Z., Lu, H., & Hua, X. S. (2019). Attribute-driven feature disentangling and temporal aggregation for video person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 4913-4922).Google ScholarCross Ref
- Zheng, L., Bie, Z., Sun, Y., Wang, J., Su, C., Wang, S., & Tian, Q. (2016). Mars: A video benchmark for large-scale person re-identification. In Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part VI 14 (pp. 868-884). Springer International Publishing.Google ScholarCross Ref
- Yang, J., Zheng, W. S., Yang, Q., Chen, Y. C., & Tian, Q. (2020). Spatial-temporal graph convolutional network for video-based person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 3289-3299).Google ScholarCross Ref
- Liu, X., Zhang, P., Yu, C., Lu, H., & Yang, X. (2021). Watching you: Global-guided reciprocal learning for video-based person re-identification. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 13334-13343).Google ScholarCross Ref
- Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., ... & Guo, B. (2021). Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision (pp. 10012-10022).Google ScholarCross Ref
- Liu, C. T., Wu, C. W., Wang, Y. C. F., & Chien, S. Y. (2019). Spatially and temporally efficient non-local attention network for video-based person re-identification. arXiv preprint arXiv:1908.01683.Google Scholar
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., ... & Houlsby, N. (2010). An image is worth 16\(\times\) 16 words: transformers for image recognition at scale (2020). arXiv preprint arXiv:2010.11929.Google Scholar
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