Smart City Gnosys
Smart city article details
| Title | End-Edge-Cloud Collaborative System: A Video Big Data Processing And Analysis Architecture |
|---|---|
| ID_Doc | 23103 |
| Authors | Xing P.; Wang Y.; Peng P.; Tian Y.; Huang T. |
| Year | 2020 |
| Published | Proceedings - 3rd International Conference on Multimedia Information Processing and Retrieval, MIPR 2020 |
| DOI | http://dx.doi.org/10.1109/MIPR49039.2020.00055 |
| Abstract | In the current surveillance system, video streams are firstly captured and compressed at the cameras, and then transmitted to the backend severs or cloud for big data analysis. It is impractical to aggregate all video streams from hundreds of thousands of cameras for big data analysis. Transcoding the videos to low-bitrate ones is the conventional solution to solve the aggregation bottleneck. However, it is recognized that transcoding will inevitably affect visual feature extraction, consequently degrading the subsequent analysis performance. To address these challenges, we thus propose a new video big data analysis framework, called end-edge-cloud collaborative system. Under the end-edge-cloud collaborative framework, a camera can output two streams simultaneously, including a compressed video stream for viewing and data storage, and a compact feature stream extracted from the original video signals for visual analysis. Video stream and feature stream are synchronized by unified identification. We identify three key technologies to enable the end-edge-cloud collaborative system, including analysis-friendly video coding, visual feature compact descriptor, and user-defined neural network and parameter updating. By real-Time feeding only the feature streams into the cloud center, these cameras thus form a large-scale brain-like vision system for the smart city. A prototype has been implemented to demonstrate its feasibility. Experiment results show that our system can achieve high efficient video compression and guarantee the analysis performance. Furthermore, our system makes the big data analysis feasible which only need aggregate low bit-rate com-pressed feature stream. © 2020 IEEE. |
| Author Keywords | Analysis-friendly Video Coding; City Brain; Collaborative; Compact Feature Representation; Data Aggregation |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
14727  | 0.881 | Pasandi H.B.; Nadeem T. | Collaborative Intelligent Cross-Camera Video Analytics At Edge: Opportunities And Challenges | AIChallengeIoT 2019 - Proceedings of the 2019 International Workshop on Challenges in Artificial Intelligence and Machine Learning for Internet of Things (2019) |
| 3217 | 0.872 | Mendieta M.; Neff C.; Lingerfelt D.; Beam C.; George A.; Rogers S.; Ravindran A.; Tabkhi H. | A Novel Application/Infrastructure Co-Design Approach For Real-Time Edge Video Analytics | Conference Proceedings - IEEE SOUTHEASTCON, 2019-April (2019) |
| 44025 | 0.865 | Zhong J.; Niu Y.; Zhu M. | Queryedge: Real-Time Muti-Video Query In Edge-Cloud Collaborative System | Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics (2023) |
| 38148 | 0.865 | Qi Z.; Du L.; Huo R.; Yang F.; Huang T. | Multi-Camera Video Collaborative Analysis Method Based On Edge Computing; [基于边缘计算的多摄像头视频协同分析方法] | Tongxin Xuebao/Journal on Communications, 44, 8 (2023) |
| 47408 | 0.859 | Rong C.; Wang J.H.; Liu J.; Wang J.; Li F.; Huang X. | Scheduling Massive Camera Streams To Optimize Large-Scale Live Video Analytics | IEEE/ACM Transactions on Networking, 30, 2 (2022) |
| 5117 | 0.858 | Bellavista P.; Chatzimisios P.; Foschini L.; Paradisioti M.; Scotece D. | A Support Infrastructure For Machine Learning At The Edge In Smart City Surveillance | Proceedings - IEEE Symposium on Computers and Communications, 2019-June (2019) |
| 48288 | 0.855 | Gao Y.; Zhang B. | Semantics-Driven Cloud-Edge Collaborative Inference A Case Study Of License Plate Detection | 2023 International Conference on Image Processing, Computer Vision and Machine Learning, ICICML 2023 (2023) |
| 34199 | 0.853 | Righetti F.; Vallati C.; Roy N.; Anastasi G. | Is Edge Computing Always Suitable For Image Analysis? An Experimental Analysis | Proceedings - 2024 IEEE 25th International Symposium on a World of Wireless, Mobile and Multimedia Networks, WoWMoM 2024 (2024) |