# A Survey on Video Analytics in Cloud-Edge-Terminal Collaborative Systems

arXiv: https://arxiv.org/abs/2502.06581 · HTML: https://mlsanity.com/paper/2502.06581/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 57 references that are in the map.
- First posted on arXiv 10 Feb 2025.

## Builds on (most cited first, 30 of 44)

- [Video-Bench: A Comprehensive Benchmark and Toolkit for Evaluating Video-based Large Language Models](https://mlsanity.com/paper/2311.16103/) (2023)
- [Video-LaVIT: Unified Video-Language Pre-training with Decoupled Visual-Motional Tokenization](https://mlsanity.com/paper/2402.03161/) (2024)
- [Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services](https://mlsanity.com/paper/2303.16129/) (2023)
- [Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models](https://mlsanity.com/paper/2302.05087/) (2023)
- [Ekya: Continuous Learning of Video Analytics Models on Edge Compute Servers](https://mlsanity.com/paper/2012.10557/) (2020)
- [End-to-End Learning of Motion Representation for Video Understanding](https://mlsanity.com/paper/1804.00413/) (2018)
- [TransFlow: Transformer as Flow Learner](https://mlsanity.com/paper/2304.11523/) (2023)
- [Towards Open Set Video Anomaly Detection](https://mlsanity.com/paper/2208.11113/) (2022)
- [DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network](https://mlsanity.com/paper/2211.00882/) (2022)
- [Unsupervised Anomaly Localization with Structural Feature-Autoencoders](https://mlsanity.com/paper/2208.10992/) (2022)
- [Symbolic Pregression: Discovering Physical Laws from Distorted Video](https://mlsanity.com/paper/2005.11212/) (2020)
- [Massively Parallel Video Networks](https://mlsanity.com/paper/1806.03863/) (2018)
- [Llama: A Heterogeneous & Serverless Framework for Auto-Tuning Video Analytics Pipelines](https://mlsanity.com/paper/2102.01887/) (2021)
- [CROSSBOW: Scaling Deep Learning with Small Batch Sizes on Multi-GPU Servers](https://mlsanity.com/paper/1901.02244/) (2019)
- [REVAMP2T: Real-time Edge Video Analytics for Multi-camera Privacy-aware Pedestrian Tracking](https://mlsanity.com/paper/1911.09217/) (2019)
- [Fed-FSNet: Mitigating Non-I.I.D. Federated Learning via Fuzzy Synthesizing Network](https://mlsanity.com/paper/2208.12044/) (2022)
- [iGniter: Interference-Aware GPU Resource Provisioning for Predictable DNN Inference in the Cloud](https://mlsanity.com/paper/2211.01713/) (2022)
- [Variance-Covariance Regularization Improves Representation Learning](https://mlsanity.com/paper/2306.13292/) (2023)
- [Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning](https://mlsanity.com/paper/2306.15333/) (2023)
- [Video Anomaly Detection by Estimating Likelihood of Representations](https://mlsanity.com/paper/2012.01468/) (2020)
- [A Serverless Cloud-Fog Platform for DNN-Based Video Analytics with Incremental Learning](https://mlsanity.com/paper/2102.03012/) (2021)
- [Equilibrium in the Computing Continuum through Active Inference](https://mlsanity.com/paper/2311.16769/) (2023)
- [Beyond Transfer Learning: Co-finetuning for Action Localisation](https://mlsanity.com/paper/2207.03807/) (2022)
- [Ego-Vehicle Action Recognition based on Semi-Supervised Contrastive Learning](https://mlsanity.com/paper/2303.00977/) (2023)
- [Turbo: Opportunistic Enhancement for Edge Video Analytics](https://mlsanity.com/paper/2207.00172/) (2022)
- [Automatic Pruning for Quantized Neural Networks](https://mlsanity.com/paper/2002.00523/) (2020)
- [Single-path Bit Sharing for Automatic Loss-aware Model Compression](https://mlsanity.com/paper/2101.04935/) (2021)
- [perf4sight: A toolflow to model CNN training performance on Edge GPUs](https://mlsanity.com/paper/2108.05580/) (2021)
- [Sustainable AI Processing at the Edge](https://mlsanity.com/paper/2207.01209/) (2022)
- [Hyperspherical Quantization: Toward Smaller and More Accurate Models](https://mlsanity.com/paper/2212.12653/) (2022)

## Built on by (most cited first, 2 of 2)

- [FedVideoMAE: Efficient Federated Video Moderation with Differential Privacy and Secure Aggregation](https://mlsanity.com/paper/2512.18809/) (2025)
- [Efficient Reasoning Distillation: Small Video-Language Models via Synthetic CoT and Difficulty-Aware Fine-Tuning](https://mlsanity.com/paper/2609.16255/) (2026)

## Abstract (arXiv)

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.