Development and Application of Cloud-Based Storage Solutions in Modern Video Surveillance Systems
Keywords:
Cloud storage technology, Video surveillanc, Development, ApplicationAbstract
Cloud storage employs a cloud-based data storage deployment model and distributed computing methodologies to aggregate a large number of heterogeneous data storage devices within the network through application software, thereby enabling effective and rational unified computation and data processing of resources and information. End users can access cloud storage data resources and business systems via remote or virtual access to desktop software applications and programmatic interfaces, thereby facilitating efficient and rapid resource analysis and data processing within large-scale data storage and access environments. Accordingly, this article provides a focused analysis of the development and application of cloud storage technology in the context of video surveillance.
References
Yang, Z., Zhang, W., Lin, X., Zhang, Y., & Li, S. (2023, April). HGMatch: A Match-by-Hyperedge Approach for Subgraph Matching on Hypergraphs. In 2023 IEEE 39th International Conference on Data Engineering (ICDE) (pp. 2063-2076). IEEE.
Ukey, N., Zhang, G., Yang, Z., Li, B., Li, W., & Zhang, W. (2023). Efficient continuous kNN join over dynamic high-dimensional data. World Wide Web, 26(6), 3759-3794.
Lian, J., & Chen, T. (2024). Research on Complex Data Mining Analysis and Pattern Recognition Based on Deep Learning. Journal of Computing and Electronic Information Management, 12(3), 37-41.
Zhou, Z. (2025, November). Digital precision distribution strategy for social media content on private domain platforms in the automotive industry: a collaborative filtering model based on user behavior. In Proceedings of the 2025 International Conference on Digital Society and Intelligent Computing (pp. 516-521).
Wensi, L. (2026). AI-Assisted Marketing Content Generation for Non-Standard Industrial Automation Solutions. Journal of Economic Theory and Business Management, 3(1), 18-25.
Ren, Z. (2024). Adaptive Multi-Scale Fusion for Infrared and Visible Object Detection in YOLOv8. Journal of Theory and Practice of Engineering Science, 4(09), 28–34. https://doi.org/10.53469/jtpes.2024.04(09).04
Zhao, S., Lin, Y., Yang, X., Lu, Q., Xue, H., & Jiang, G. (2025). Optimization of Deep Learning Models for Dynamic Market Behavior Prediction. arXiv preprint arXiv:2511.19090.
Yang, X., Zheng, X., & Lu, Q. (2025, October). Construction and early warning of multi-dimensional network credit-related transaction risk maps by integrating graph neural network (GNN). In Proceedings of the 2025 2nd International Conference on Digital Economy and Computer Science (pp. 919-923).
Shen, Zepeng, et al. "Research on Application of Whale Optimization Algorithm in Financial Payment Fraud Detection." 2025 4th International Conference on Artificial Intelligence, Internet and Digital Economy (ICAID). IEEE, 2025.
Tian, Q., Wang, Z., & Cui, X. (2024). Improved Unet brain tumor image segmentation based on GSConv module and ECA attention mechanism. arXiv preprint arXiv:2409.13626.
Deng, X., & Yang, J. (2025, August). Multi-Layer Defense Strategies and Privacy Preserving Enhancements for Membership Reasoning Attacks in a Federated Learning Framework. In 2025 5th International Conference on Computer Science and Blockchain (CCSB) (pp. 278-282). IEEE.
Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
Sun, L. (2025, November). Adaptive Interfaces for Personalized User Experience: A Machine Learning Approach. In Proceedings of the 2025 International Conference on Artificial Intelligence and Sustainable Development (pp. 457-462).
Yan, H., Wang, Z., Xu, Z., Wang, Z., Wu, Z., & Lyu, R. (2024, July). Research on image super-resolution reconstruction mechanism based on convolutional neural network. In Proceedings of the 2024 4th International Conference on Artificial Intelligence, Automation and High Performance Computing (pp. 142-146).
Pang, H., Zhou, L., Dong, Y., Chen, P., Gu, D., Lyu, T., & Zhang, H. (2024). Electronic Health Records-Based Data-Driven Diabetes Knowledge Unveiling and Risk Prognosis. arXiv preprint arXiv:2412.03961.