A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems
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arXiv
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| Main Authors: | , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910904010932224 |
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| author | Gong, Linxiao Yang, Hao Fang, Gaoyun Ju, Bobo Guo, Juncen Zhu, Xiaoguang Hu, Xiping Wang, Yan Sun, Peng Boukerche, Azzedine |
| author_facet | Gong, Linxiao Yang, Hao Fang, Gaoyun Ju, Bobo Guo, Juncen Zhu, Xiaoguang Hu, Xiping Wang, Yan Sun, Peng Boukerche, Azzedine |
| contents | The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis. Among multiple advantages, CETC systems can distribute video processing tasks and enable adaptive analytics across cloud, edge, and terminal devices, leading to breakthroughs in video surveillance, autonomous driving, and smart cities. In this survey, we first analyze fundamental architectural components, including hierarchical, distributed, and hybrid frameworks, alongside edge computing platforms and resource management mechanisms. Building upon these foundations, edge-centric approaches emphasize on-device processing, edge-assisted offloading, and edge intelligence, while cloud-centric methods leverage powerful computational capabilities for complex video understanding and model training. Our investigation also covers hybrid video analytics incorporating adaptive task offloading and resource-aware scheduling techniques that optimize performance across the entire system. Beyond conventional approaches, recent advances in large language models and multimodal integration reveal both opportunities and challenges in platform scalability, data protection, and system reliability. Future directions also encompass explainable systems, efficient processing mechanisms, and advanced video analytics, offering valuable insights for researchers and practitioners in this dynamic field. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2502_06581 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems Gong, Linxiao Yang, Hao Fang, Gaoyun Ju, Bobo Guo, Juncen Zhu, Xiaoguang Hu, Xiping Wang, Yan Sun, Peng Boukerche, Azzedine Networking and Internet Architecture Computer Vision and Pattern Recognition Machine Learning The explosive growth of video data has driven the development of distributed video analytics in cloud-edge-terminal collaborative (CETC) systems, enabling efficient video processing, real-time inference, and privacy-preserving analysis. Among multiple advantages, CETC systems can distribute video processing tasks and enable adaptive analytics across cloud, edge, and terminal devices, leading to breakthroughs in video surveillance, autonomous driving, and smart cities. In this survey, we first analyze fundamental architectural components, including hierarchical, distributed, and hybrid frameworks, alongside edge computing platforms and resource management mechanisms. Building upon these foundations, edge-centric approaches emphasize on-device processing, edge-assisted offloading, and edge intelligence, while cloud-centric methods leverage powerful computational capabilities for complex video understanding and model training. Our investigation also covers hybrid video analytics incorporating adaptive task offloading and resource-aware scheduling techniques that optimize performance across the entire system. Beyond conventional approaches, recent advances in large language models and multimodal integration reveal both opportunities and challenges in platform scalability, data protection, and system reliability. Future directions also encompass explainable systems, efficient processing mechanisms, and advanced video analytics, offering valuable insights for researchers and practitioners in this dynamic field. |
| title | A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems |
| topic | Networking and Internet Architecture Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2502.06581 |