Abstract
With the arrival of big data era and the rapid development of artificial intelligence, deep learning has made breakthroughs in many fields. However, although it has been widely used in many fields, there are still many challenges in itself, such as the slow reference speed of neural networks. In stream processing scenarios that integrate deep learning algorithms, data is often massive and generated with high-speed, requiring the system to respond in seconds or even milliseconds. If the response speed is too slow, the actual application requirements may not be met, and the user experience cannot be guaranteed. How to use stream processing technology to improve the speed and throughput of such systems has become an urgent problem to be solved. This paper used the popular real-time stream processing framework Flink to implement a complete data stream processing program, and integrated three algorithms of face detection, facial key points detection and face mosaic into the processing logic. Setting the operator parallelism realized parallel processing of video data, which improved the system throughput. The user can choose which algorithm to perform on the video, and can also choose the parallelism according to the performance of the machine. The system implemented the Flink framework to process video in parallel, achieved the effect of improving processing efficiency, and completely implemented the front-end interface and back-end program.
This work was supported by Hubei Innovation and Entrepreneurship Training Program for University Students under Grant NO. S202210488057.
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References
Li, Y., Gai, K., et al.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. Multimed. Comp. Comm. App. 12(4s), 1–18 (2016)
Qiu, M., Chen, Z., Ming, Z., Qin, X., Niu, J.: Energy-aware data allocation with hybrid memory for mobile cloud systems. IEEE Syst. J. 11(2), 813–822 (2014)
Qiu, M., Li, H., Sha, E.: Heterogeneous real-time embedded software optimization considering hardware platform. ACM Sym. Appl. Comp. 1637–1641 (2009)
Qiu, M., Xue, C., Shao, Z., Sha, E.: Energy minimization with soft real-time and DVS for uniprocessor and multiprocessor embedded systems. In: IEEE DATE Conference, pp. 1–6 (2007)
Niu, J., Gao, Y., et al.: Selecting proper wireless network interfaces for user experience enhancement with guaranteed probability. JPDC 72(12), 1565–1575 (2012)
Qiu, M., Xue, C., Shao, Z., et al.: Efficient algorithm of energy minimization for heterogeneous wireless sensor network. In: IEEE EUC, pp. 25–34 (2006)
Gai, K., Qiu, M., Elnagdy, S.: A novel secure big data cyber incident analytics framework for cloud-based cybersecurity insurance. In: IEEE BigData Security (2016)
Qiu, H., Dong, T., Zhang, T., et al.: Adversarial attacks against network intrusion detection in IoT systems. IEEE IoT J. 8(13), 10327–10335 (2020)
Qiu, M., Qiu, H.: Review on image processing based adversarial example defenses in computer vision. In: IEEE Conference on BigData Security, pp. 94–99, Baltimore, MD, USA (2020)
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., Bharath, A.A.: Generative adversarial networks: an overview. IEEE Signal Proc. Mag. 35(1), 53–65 (2018)
Li, J., Ming, Z., et al.: Resource allocation robustness in multi-core embedded systems with inaccurate information. J. Syst. Arch. 57(9), 840–849 (2011)
Qiu, M., Jia, Z., et al.: Voltage assignment with guaranteed probability satisfying timing constraint for real-time multiproceesor DSP. J. Signal Proc. Sys. 46, 55–73 (2007)
Qiu, M., Yang, L., Shao, Z., Sha, E.: Dynamic and leakage energy minimization with soft real-time loop scheduling and voltage assignment. IEEE TVLSI 18(3), 501–504 (2009)
Tantalaki, N., Souravlas, S., Roumeliotis, M.: A review on big data real-time stream processing and its scheduling techniques. Int. J. Parallel Emerg. Distrib. Syst. 35(5), 571–601 (2020)
Memeti, S., Pllana, S., Binotto, A., et al.: Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review. Computing 101, 893–936 (2019)
Isah, H., Abughofa, T., Mahfuz, S., et al.: A survey of distributed data stream processing frameworks. IEEE Access 7, 154300–154316 (2019)
Calavaro, C., Russo, G.R., Cardellini, V.: Real-time analysis of market data leveraging Apache Flink. In: 16th ACM International Conference on Distributed and Event-Based Systems (DEBS), New York, NY, USA, pp. 162–165 (2022)
Xu, B., Jiang, J., Ye, J.: Information intelligence system solution based on Big Data Flink technology. In: 2022 ACM 4th International Conference on Big Data Engineering (BDE), New York, NY, USA, pp. 21–26 (2022)
HoseinyFarahabady, M.R., Jannesari, A., et al.: Q-Flink: A QoS-Aware Controller for Apache Flink. In: 20th IEEE/ACM Symposium on Cluster, Cloud and Internet Computing (CCGRID), pp. 629–638 (2020)
Ha, T.W., Kang, J.M., Kim, M.H.: Real-time deep learning-based anomaly detection approach for multivariate data streams with Apache Flink. In: Bakaev, M., Ko, I.-Y., Mrissa, M., Pautasso, C., Srivastava, A. (eds.) ICWE 2021. CCIS, vol. 1508, pp. 39–49. Springer, Cham (2022). https://doi.org/10.1007/978-3-030-92231-3_4
Dong, Y., Wang, R., He, J.: Real-time network intrusion detection system based on deep learning. In: IEEE 10th International Conference on Software Engineering and Service Science (ICSESS), pp. 1–4 (2019)
Del Monte, B., Prodan, R.: A scalable GPU-enabled framework for training deep neural networks. In: 2nd International Conference on Green High Performance Computing (ICGHPC), pp. 1–8 (2016)
Hu, F., Lakdawala, S., et al.: Low-power, intelligent sensor hardware interface for medical data preprocessing. IEEE Trans Inf. Tech. Biomed. 13(4), 656–663 (2009)
Qiu, H., Zheng, Q., et al.: Topological graph convolutional network-based urban traffic flow and density prediction. IEEE Trans. Intel. Trans. Sys. 22(7), 4560–4569 (2021)
Li, Y., Song, Y., et al.: Intelligent fault diagnosis by fusing domain adversarial training and maximum mean discrepancy via ensemble learning. IEEE Trans. Indus. Inform. 17(4), 2833–2841 (2021)
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Zhao, Q., Wu, J., Wu, X., Fan, J., Wang, L., Wu, F. (2023). Design and Implementation of Deep Learning Real-Time Streaming Video Data Processing System. In: Qiu, M., Lu, Z., Zhang, C. (eds) Smart Computing and Communication. SmartCom 2022. Lecture Notes in Computer Science, vol 13828. Springer, Cham. https://doi.org/10.1007/978-3-031-28124-2_1
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